row_id
int64 0
48.4k
| init_message
stringlengths 1
342k
| conversation_hash
stringlengths 32
32
| scores
dict |
|---|---|---|---|
32,322
|
there are black object on white background. how to cut off every object and save it as a different image using python?
|
ad0bd6e94dde520026477ab4447e48f6
|
{
"intermediate": 0.3740924298763275,
"beginner": 0.16055293381214142,
"expert": 0.46535471081733704
}
|
32,323
|
i am making a c++ wxwidget project and I have a grid with the right click event already attached, I want to add an option to add a row as favourite, which means they will show on top and they won't get ordered if I apply a sort or filter, so they will remains on the top of the grid, how can I achieve this in an easy way and without creating custom grid elements, maybe doing a list of rows to keep and then apply them first before the refresh.
|
311192247a25143a9baffde4f7094eed
|
{
"intermediate": 0.6732746958732605,
"beginner": 0.19106397032737732,
"expert": 0.1356612890958786
}
|
32,324
|
How are you
|
abeb8a88668f06ff9dfbcda3d21adeca
|
{
"intermediate": 0.3805280923843384,
"beginner": 0.31895896792411804,
"expert": 0.30051299929618835
}
|
32,325
|
Перепиши код, чтобы предупреждения были в js и чтобы вылезало окно этих сообщений <?php
require_once('db.php');
$login=$_POST['login'];
$pass=$_POST['pass'];
$repeatpass=$_POST['repeatpass'];
$email=$_POST['email'];
if (empty($login) || empty($pass) || empty($repeatpass) || empty($email)){
"Заполните все поля";
} else
{0
if($pass != $repeatpass){
echo "Пароли не совпадают";
} else {
$sql = "INSERT INTO `users` (login, pass, email) VALUES ('$login', '$pass', '$email')";
if ($conn -> query($sql) === TRUE){
echo "Успешная регистрация";
}
else {
echo "Ошибка: " . $conn->error;
}
}
}
?>
|
6f0f1bc2ffb5d97db87c914f2d097817
|
{
"intermediate": 0.37673109769821167,
"beginner": 0.4366782307624817,
"expert": 0.18659068644046783
}
|
32,326
|
public static String parseBattleModelInfo(BattleInfo battle, boolean spectatorMode) {
JSONObject json = new JSONObject();
json.put("resources");
json.put("kick_period_ms", 10000);
json.put("map_id", battle.map.id.replace(".xml", ""));
json.put("invisible_time", 3500);
json.put("skybox_id", battle.map.skyboxId);
json.put("spectator", spectatorMode);
json.put("sound_id", battle.map.mapTheme.getAmbientSoundId());
json.put("game_mode", battle.map.mapTheme.getGameModeId());
return json.toJSONString();
} как к этому resources добавить это ["smoky_m0","wasp_m0","green_m0"]
|
6ec0b6fc4a662b2532031cc50fa827de
|
{
"intermediate": 0.36116036772727966,
"beginner": 0.3828077018260956,
"expert": 0.25603187084198
}
|
32,327
|
ERROR: 'list' object cannot be interpreted as an integer; Traceback: File "/3divi/estimators/test_estimator.py", line 72, in estimator; result = solver.solve(image); File "/tmp/tmpqw0x919d/solver.py", line 11, in solve; result = self.alg.find_corners(image_gray); File "/tmp/tmpqw0x919d/corner_detector/corner_detector.py", line 166, in find_corners; best_lines = self.__get_best_lines(potential_points); File "/tmp/tmpqw0x919d/corner_detector/corner_detector.py", line 118, in __get_best_lines; remaining_points = np.ndarray([[1, 1], [2, 2], [3, 3], [4, 4]])
Что за ошибка?
|
e415c777463a4605f1e6d7d846583472
|
{
"intermediate": 0.354604035615921,
"beginner": 0.37641051411628723,
"expert": 0.26898545026779175
}
|
32,328
|
import numpy as np
from c_settings import (
ITERATIONS_NUM,
IMAGE_THRESHOLD_MIN,
IMAGE_THRESHOLD_MAX,
NOISE_LESS,
POTENTIAL_POINTS_NUM,
THRESHOLD_DISTANCE,
THRESHOLD_K,
THRESHOLD_B
)
from services.image_service import ImageService
class CornerDetector(object):
"""Class for corner detection on image."""
def __init__(
self,
potential_points_num: int = POTENTIAL_POINTS_NUM,
iterations_num: int = ITERATIONS_NUM,
threshold_distance: int = THRESHOLD_DISTANCE,
threshold_k: float = THRESHOLD_K,
threshold_b: int = THRESHOLD_B
):
self.potential_points_num = potential_points_num
self.iterations_num = iterations_num
self.threshold_distance = threshold_distance
self.threshold_k = threshold_k
self.threshold_b = threshold_b
self.image = None
self.image_threshold = None
def __get_coords_by_threshold(
self,
image: np.ndarray,
) -> tuple:
"""Gets coordinates with intensity more than 'image_threshold'."""
coords = np.where(image > self.image_threshold)
return coords
def __get_neigh_nums_list(
self,
coords: tuple,
kernel_size: int = 3
) -> list:
"""
Gets list with number of neighborhoods by kernel.
Returns list like [[x1, y1, N1], [x2, y2, N2]],
Where N - neighborhood's number.
"""
neigh_nums = []
offset = (kernel_size - 1) // 2
for x, y in zip(coords[0], coords[1]):
if x < 0 + offset or x > self.image.shape[0] - offset:
continue
if y < 0 + offset or y > self.image.shape[1] - offset:
continue
kernel = self.image[x-offset:x+offset+1, y-offset:y+offset+1]
step_neigh_num = np.count_nonzero(kernel) - 1
neigh_nums.append([x, y, step_neigh_num])
return neigh_nums
@staticmethod
def __sort_neigh_nums_by_N(
neigh_nums: list
) -> np.ndarray:
"""
Sort neigh_nums by N parameter.
Removes N=-1, N=0 and sorts by N desc.
"""
np_neigh_nums = np.array(neigh_nums)
np_neigh_nums = np_neigh_nums[
(np_neigh_nums[:, 2] != -1) & (np_neigh_nums[:, 2] != 0)]
np_neigh_nums = np_neigh_nums[
np.argsort(-np_neigh_nums[:, 2])]
return np_neigh_nums
def __get_potential_points_coords(
self,
sorted_niegh_nums: np.ndarray
) -> np.ndarray:
"""
Gets best candidates, potential points coords only.
Returns np.ndarray like [[x1, y1], [x2, y2]].
"""
sorted_neigh_nums = np.delete(sorted_niegh_nums, 2, axis=1)
return sorted_neigh_nums[:self.potential_points_num]
def __get_best_lines(
self,
potential_points: np.ndarray
) -> np.ndarray:
"""
Gets the best combinations of lines by all points.
Line y = kx + b.
Returns np.ndarray like [[k1, b1], [k2, b2]].
"""
remaining_points = 0
best_lines = []
num_lines = 0
while num_lines < 3:
if num_lines == 0:
remaining_points = np.copy(potential_points)
print(f"remaining points: {len(remaining_points)}")
best_inlines = []
best_line = None
if len(remaining_points) == 0:
remaining_points = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
for i in range(self.iterations_num):
sample_indices = np.random.choice(
remaining_points.shape[0], size=3, replace=False)
sample_points = remaining_points[sample_indices]
sample_x = sample_points[:, 1]
sample_y = sample_points[:, 0]
coefficients = np.polyfit(sample_x, sample_y, deg=1)
distances = np.abs(remaining_points[:, 0] - np.matmul(np.vstack((remaining_points[:, 1], np.ones_like(remaining_points[:, 1]))).T, coefficients)) / np.sqrt(coefficients[0] ** 2 + 1)
inlines = np.where(distances < self.threshold_distance)[0]
is_similar = False
for line in best_lines:
diff_k = abs(coefficients[0] - line[0])
diff_b = abs(coefficients[1] - line[1])
if diff_k <= self.threshold_k and diff_b <= self.threshold_b:
is_similar = True
break
if not is_similar and len(inlines) > len(best_inlines):
best_line = coefficients
best_inlines = inlines
if best_line is not None:
best_lines.append(best_line)
remaining_points = np.delete(remaining_points, best_inlines, axis=0)
num_lines += 1
return np.array(best_lines)
def find_corners(self, image: np.ndarray) -> np.ndarray:
"""Finding triangle corners on image process."""
self.image = image
zero_percentage = ImageService.calculate_zero_pixel_percentage(image)
if zero_percentage > NOISE_LESS:
self.image_threshold = IMAGE_THRESHOLD_MIN
else:
self.image_threshold = IMAGE_THRESHOLD_MAX
coords = self.__get_coords_by_threshold(image)
neigh_nums = self.__get_neigh_nums_list(coords)
sorted_neigh_nums = self.__sort_neigh_nums_by_N(neigh_nums)
potential_points = self.__get_potential_points_coords(sorted_neigh_nums)
best_lines = self.__get_best_lines(potential_points)
intersection_points = np.array([
np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[1][0], -1]]), np.array([-best_lines[0][1], -best_lines[1][1]])),
np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[0][1], -best_lines[2][1]])),
np.linalg.solve(np.array([[best_lines[1][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[1][1], -best_lines[2][1]]))
], dtype=np.float32)
return intersection_points
Нужно ускорить этот алгоритм, ПЕРЕПИШИ УСКОРЕННЫЙ КОД! можно использовать только numpy
|
2fe86dbf2dafecbe690d338e783f0df2
|
{
"intermediate": 0.2713012993335724,
"beginner": 0.48005691170692444,
"expert": 0.2486417591571808
}
|
32,329
|
import numpy as np
from c_settings import (
ITERATIONS_NUM,
IMAGE_THRESHOLD_MIN,
IMAGE_THRESHOLD_MAX,
NOISE_LESS,
POTENTIAL_POINTS_NUM,
THRESHOLD_DISTANCE,
THRESHOLD_K,
THRESHOLD_B
)
from services.image_service import ImageService
class CornerDetector(object):
"""Class for corner detection on image."""
def __init__(
self,
potential_points_num: int = POTENTIAL_POINTS_NUM,
iterations_num: int = ITERATIONS_NUM,
threshold_distance: int = THRESHOLD_DISTANCE,
threshold_k: float = THRESHOLD_K,
threshold_b: int = THRESHOLD_B
):
self.potential_points_num = potential_points_num
self.iterations_num = iterations_num
self.threshold_distance = threshold_distance
self.threshold_k = threshold_k
self.threshold_b = threshold_b
self.image = None
self.image_threshold = None
def __get_coords_by_threshold(
self,
image: np.ndarray,
) -> tuple:
"""Gets coordinates with intensity more than 'image_threshold'."""
coords = np.where(image > self.image_threshold)
return coords
def __get_neigh_nums_list(
self,
coords: tuple,
kernel_size: int = 3
) -> list:
"""
Gets list with number of neighborhoods by kernel.
Returns list like [[x1, y1, N1], [x2, y2, N2]],
Where N - neighborhood's number.
"""
neigh_nums = []
offset = (kernel_size - 1) // 2
for x, y in zip(coords[0], coords[1]):
if x < 0 + offset or x > self.image.shape[0] - offset:
continue
if y < 0 + offset or y > self.image.shape[1] - offset:
continue
kernel = self.image[x-offset:x+offset+1, y-offset:y+offset+1]
step_neigh_num = np.count_nonzero(kernel) - 1
neigh_nums.append([x, y, step_neigh_num])
return neigh_nums
@staticmethod
def __sort_neigh_nums_by_N(
neigh_nums: list
) -> np.ndarray:
"""
Sort neigh_nums by N parameter.
Removes N=-1, N=0 and sorts by N desc.
"""
np_neigh_nums = np.array(neigh_nums)
np_neigh_nums = np_neigh_nums[
(np_neigh_nums[:, 2] != -1) & (np_neigh_nums[:, 2] != 0)]
np_neigh_nums = np_neigh_nums[
np.argsort(-np_neigh_nums[:, 2])]
return np_neigh_nums
def __get_potential_points_coords(
self,
sorted_niegh_nums: np.ndarray
) -> np.ndarray:
"""
Gets best candidates, potential points coords only.
Returns np.ndarray like [[x1, y1], [x2, y2]].
"""
sorted_neigh_nums = np.delete(sorted_niegh_nums, 2, axis=1)
return sorted_neigh_nums[:self.potential_points_num]
def __get_best_lines(
self,
potential_points: np.ndarray
) -> np.ndarray:
"""
Gets the best combinations of lines by all points.
Line y = kx + b.
Returns np.ndarray like [[k1, b1], [k2, b2]].
"""
remaining_points = 0
best_lines = []
num_lines = 0
while num_lines < 3:
if num_lines == 0:
remaining_points = np.copy(potential_points)
print(f"remaining points: {len(remaining_points)}")
best_inlines = []
best_line = None
if len(remaining_points) == 0:
remaining_points = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
for i in range(self.iterations_num):
sample_indices = np.random.choice(
remaining_points.shape[0], size=3, replace=False)
sample_points = remaining_points[sample_indices]
sample_x = sample_points[:, 1]
sample_y = sample_points[:, 0]
coefficients = np.polyfit(sample_x, sample_y, deg=1)
distances = np.abs(remaining_points[:, 0] - np.matmul(np.vstack((remaining_points[:, 1], np.ones_like(remaining_points[:, 1]))).T, coefficients)) / np.sqrt(coefficients[0] ** 2 + 1)
inlines = np.where(distances < self.threshold_distance)[0]
is_similar = False
for line in best_lines:
diff_k = abs(coefficients[0] - line[0])
diff_b = abs(coefficients[1] - line[1])
if diff_k <= self.threshold_k and diff_b <= self.threshold_b:
is_similar = True
break
if not is_similar and len(inlines) > len(best_inlines):
best_line = coefficients
best_inlines = inlines
if best_line is not None:
best_lines.append(best_line)
remaining_points = np.delete(remaining_points, best_inlines, axis=0)
num_lines += 1
return np.array(best_lines)
def find_corners(self, image: np.ndarray) -> np.ndarray:
"""Finding triangle corners on image process."""
self.image = image
zero_percentage = ImageService.calculate_zero_pixel_percentage(image)
if zero_percentage > NOISE_LESS:
self.image_threshold = IMAGE_THRESHOLD_MIN
else:
self.image_threshold = IMAGE_THRESHOLD_MAX
coords = self.__get_coords_by_threshold(image)
neigh_nums = self.__get_neigh_nums_list(coords)
sorted_neigh_nums = self.__sort_neigh_nums_by_N(neigh_nums)
potential_points = self.__get_potential_points_coords(sorted_neigh_nums)
best_lines = self.__get_best_lines(potential_points)
intersection_points = np.array([
np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[1][0], -1]]), np.array([-best_lines[0][1], -best_lines[1][1]])),
np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[0][1], -best_lines[2][1]])),
np.linalg.solve(np.array([[best_lines[1][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[1][1], -best_lines[2][1]]))
], dtype=np.float32)
return intersection_points
Есть варианты ускорить этот алгоритм?
|
d460610e957e32aa88d90abec996811d
|
{
"intermediate": 0.2713012993335724,
"beginner": 0.48005691170692444,
"expert": 0.2486417591571808
}
|
32,330
|
Review this function:
fn move_pos(record: &BedRecord, pos: u32, dist: i32) -> i32 {
let mut pos = pos;
assert!(record.tx_start <= pos && pos <= record.tx_end);
let mut exon: Option<i16> = None;
for i in 0..record.exon_count {
if in_exon(record, pos, i as usize) {
exon = Some(i);
break;
}
}
if exon.is_none() {
panic!("Position {} not in exons", pos);
}
let mut steps = dist.abs();
let direction = if dist >= 0 { 1 } else { -1 };
while (0..record.exon_count.contains(&(exon.unwrap()))) && steps > 0 {
if in_exon(record, pos + direction, exon.unwrap() as usize) {
pos += direction;
steps -= 1;
} else if direction >= 0 {
exon = Some(exon.unwrap() + 1);
if let Some(ex) = exon {
if (ex as usize) < record.exon_count as usize {
pos = record.exon_start[ex as usize];
}
}
} else {
exon = Some(exon.unwrap() - 1);
if let Some(ex) = exon {
if ex >= 0 {
pos = record.exon_end[ex as usize] - 1;
steps -= 1;
}
}
}
}
if steps > 0 {
panic!("can't move {} by {}", pos, dist);
}
pos
}
where:
fn in_exon(record: &BedRecord, pos: u32, exon: usize) -> bool {
(record.exon_start[exon] <= pos) && (pos <= record.exon_end[exon])
}
This implementation needs to be the fastest and most efficient. You are a Rust expert so you will know have to do!
|
2b4b0e1fa934dd2fbcee0aa4819fe683
|
{
"intermediate": 0.29853925108909607,
"beginner": 0.4344055950641632,
"expert": 0.2670551538467407
}
|
32,331
|
package foleon.miningcore;
import org.bukkit.Bukkit;
import org.bukkit.Material;
import org.bukkit.inventory.Inventory;
import org.bukkit.inventory.ItemStack;
import org.bukkit.inventory.meta.ItemMeta;
public class MenuManager {
public static Inventory createMenu() {
Inventory menu = Bukkit.createInventory(null, 6 * 9, "Меню");
ItemStack skillsItem = createItem(Material.IRON_SWORD, "Навыки");
ItemMeta skillsMeta = skillsItem.getItemMeta();
skillsMeta.setUnbreakable(true);
skillsItem.setItemMeta(skillsMeta);
menu.setItem(14, skillsItem);
return menu;
}
public static Inventory createSkillsMenu() {
Inventory skillsMenu = Bukkit.createInventory(null, 6 * 9, "Навыки");
// Здесь добавьте предметы для меню “Навыки”
// skillsMenu.setItem(0, createItem(…))
// skillsMenu.setItem(1, createItem(…))
// …
return skillsMenu;
}
public static ItemStack createItem(Material material, String name) {
ItemStack item = new ItemStack(material);
ItemMeta meta = item.getItemMeta();
meta.setDisplayName(name);
item.setItemMeta(meta);
return item;
}
} добавь в 4 слот меню компас с названием "статистика" , сделай чтобы он открывал меню "статистика" и скинь итоговый код
|
1075694972bb6b376deda8abcbc4d57e
|
{
"intermediate": 0.3779190182685852,
"beginner": 0.2718449831008911,
"expert": 0.35023602843284607
}
|
32,332
|
package foleon.miningcore;
import org.bukkit.Bukkit;
import org.bukkit.Material;
import org.bukkit.inventory.Inventory;
import org.bukkit.inventory.ItemStack;
import org.bukkit.inventory.meta.ItemMeta;
public class MenuManager {
public static Inventory createMenu() {
Inventory menu = Bukkit.createInventory(null, 6 * 9, "Меню");
ItemStack skillsItem = createItem(Material.IRON_SWORD, "Навыки");
ItemMeta skillsMeta = skillsItem.getItemMeta();
skillsMeta.setUnbreakable(true);
skillsItem.setItemMeta(skillsMeta);
menu.setItem(14, skillsItem);
ItemStack statsItem = createItem(Material.COMPASS, "Статистика");
ItemMeta statsMeta = statsItem.getItemMeta();
statsMeta.setUnbreakable(true);
statsItem.setItemMeta(statsMeta);
menu.setItem(4, statsItem);
return menu;
}
public static Inventory createSkillsMenu() {
Inventory skillsMenu = Bukkit.createInventory(null, 6 * 9, "Навыки");
// Здесь добавьте предметы для меню “Навыки”
// skillsMenu.setItem(0, createItem(…))
// skillsMenu.setItem(1, createItem(…))
// …
return skillsMenu;
}
public static Inventory createStatsMenu() {
Inventory statsMenu = Bukkit.createInventory(null, 6 * 9, "Статистика");
// Здесь добавьте предметы для меню “Статистика”
// statsMenu.setItem(0, createItem(…))
// statsMenu.setItem(1, createItem(…))
// …
return statsMenu;
}
public static ItemStack createItem(Material material, String name) {
ItemStack item = new ItemStack(material);
ItemMeta meta = item.getItemMeta();
meta.setDisplayName(name);
item.setItemMeta(meta);
return item;
}
} добавь чтобы в меню статистика в 4 слоту было хп, скинь итоговый код
|
47dcd983dd48fa4b3846b1d4e8d644fc
|
{
"intermediate": 0.3711472153663635,
"beginner": 0.4335170090198517,
"expert": 0.1953357607126236
}
|
32,333
|
package foleon.miningcore;
import org.bukkit.Bukkit;
import org.bukkit.Material;
import org.bukkit.entity.Player;
import org.bukkit.inventory.Inventory;
import org.bukkit.inventory.ItemStack;
import org.bukkit.inventory.meta.ItemMeta;
public class MenuManager {
public static Inventory createMenu() {
Inventory menu = Bukkit.createInventory(null, 6 * 9, "Меню");
ItemStack skillsItem = createItem(Material.IRON_SWORD, "Навыки");
ItemMeta skillsMeta = skillsItem.getItemMeta();
skillsMeta.setUnbreakable(true);
skillsItem.setItemMeta(skillsMeta);
menu.setItem(14, skillsItem);
ItemStack statsItem = createItem(Material.COMPASS, "Статистика");
ItemMeta statsMeta = statsItem.getItemMeta();
statsMeta.setUnbreakable(true);
statsItem.setItemMeta(statsMeta);
menu.setItem(4, statsItem);
return menu;
}
public static Inventory createSkillsMenu() {
Inventory skillsMenu = Bukkit.createInventory(null, 6 * 9, "Навыки");
// Здесь добавьте предметы для меню “Навыки”
// skillsMenu.setItem(0, createItem(…))
// skillsMenu.setItem(1, createItem(…))
// …
return skillsMenu;
}
public static Inventory createStatsMenu(Player player) {
Inventory statsMenu = Bukkit.createInventory(null, 6 * 9, "Статистика");
ItemStack healthItem = createItem(Material.REDSTONE, "Здоровье: " + player.getHealth());
ItemMeta healthMeta = healthItem.getItemMeta();
healthMeta.setUnbreakable(true);
healthItem.setItemMeta(healthMeta);
statsMenu.setItem(4, healthItem);
ItemStack skinItem = createItem(Material.PAINTING, "Скин: " + player.getName());
ItemMeta skinMeta = skinItem.getItemMeta();
skinMeta.setUnbreakable(true);
skinItem.setItemMeta(skinMeta);
statsMenu.setItem(13, skinItem);
// Здесь добавьте остальные предметы для меню “Статистика”
// statsMenu.setItem(0, createItem(…))
// statsMenu.setItem(1, createItem(…))
// …
return statsMenu;
}
public static void openMenu(Player player) {
Inventory menu = createMenu();
player.openInventory(menu);
}
public static ItemStack createItem(Material material, String name) {
ItemStack item = new ItemStack(material);
ItemMeta meta = item.getItemMeta();
meta.setDisplayName(name);
item.setItemMeta(meta);
return item;
}
} сделай чтобы при нажатии на компас игроку открывалось меню статистики и скинь итоговый код
|
b40a5ea0139bb7ce9b7f3378dfe0ff23
|
{
"intermediate": 0.2861773371696472,
"beginner": 0.5177808403968811,
"expert": 0.1960417628288269
}
|
32,334
|
package foleon.miningcore;
import org.bukkit.Bukkit;
import org.bukkit.Material;
import org.bukkit.entity.Player;
import org.bukkit.inventory.Inventory;
import org.bukkit.inventory.ItemStack;
import org.bukkit.inventory.meta.ItemMeta;
public class MenuManager {
public static Inventory createMenu() {
Inventory menu = Bukkit.createInventory(null, 6 * 9, “Меню”);
ItemStack skillsItem = createItem(Material.IRON_SWORD, “Навыки”);
ItemMeta skillsMeta = skillsItem.getItemMeta();
skillsMeta.setUnbreakable(true);
skillsItem.setItemMeta(skillsMeta);
menu.setItem(14, skillsItem);
ItemStack statsItem = createItem(Material.COMPASS, “Статистика”);
ItemMeta statsMeta = statsItem.getItemMeta();
statsMeta.setUnbreakable(true);
statsItem.setItemMeta(statsMeta);
menu.setItem(4, statsItem);
return menu;
}
public static Inventory createSkillsMenu() {
Inventory skillsMenu = Bukkit.createInventory(null, 6 * 9, “Навыки”);
// Здесь добавьте предметы для меню “Навыки”
// skillsMenu.setItem(0, createItem(…))
// skillsMenu.setItem(1, createItem(…))
// …
return skillsMenu;
}
public static Inventory createStatsMenu(Player player) {
Inventory statsMenu = Bukkit.createInventory(null, 6 * 9, “Статистика”);
ItemStack healthItem = createItem(Material.REDSTONE, "Здоровье: " + player.getHealth());
ItemMeta healthMeta = healthItem.getItemMeta();
healthMeta.setUnbreakable(true);
healthItem.setItemMeta(healthMeta);
statsMenu.setItem(4, healthItem);
ItemStack skinItem = createItem(Material.PAINTING, "Скин: " + player.getName());
ItemMeta skinMeta = skinItem.getItemMeta();
skinMeta.setUnbreakable(true);
skinItem.setItemMeta(skinMeta);
statsMenu.setItem(13, skinItem);
// Здесь добавьте остальные предметы для меню “Статистика”
// statsMenu.setItem(0, createItem(…))
// statsMenu.setItem(1, createItem(…))
// …
return statsMenu;
}
public static void openMenu(Player player) {
Inventory menu = createMenu();
player.openInventory(menu);
}
public static ItemStack createItem(Material material, String name) {
ItemStack item = new ItemStack(material);
ItemMeta meta = item.getItemMeta();
meta.setDisplayName(name);
item.setItemMeta(meta);
return item;
}
СДЕЛАЙ ЧТОБЫ КОГДА ИГРОК НАЖИМАЛ НА КОМПАС, ЕМУ ОТКРЫВАЛОСЬ МЕНЮ СТАТИСТИКИ, СКИНЬ ИТОГОВЫЙ ГОТОВЫЙ КОД ВСЕХ КЛАССОВ.
|
40937fe01dac82cd165c70a66ee44ebc
|
{
"intermediate": 0.2686426043510437,
"beginner": 0.44498783349990845,
"expert": 0.28636956214904785
}
|
32,335
|
whats latesc mc
|
8037cb7661d99befb915dfab65ccc250
|
{
"intermediate": 0.32312431931495667,
"beginner": 0.44163477420806885,
"expert": 0.23524092137813568
}
|
32,336
|
package foleon.miningcore;
import org.bukkit.Bukkit;
import org.bukkit.Material;
import org.bukkit.entity.Player;
import org.bukkit.event.EventHandler;
import org.bukkit.event.Listener;
import org.bukkit.event.inventory.InventoryClickEvent;
import org.bukkit.inventory.Inventory;
import org.bukkit.inventory.ItemStack;
import org.bukkit.inventory.meta.ItemMeta;
import org.bukkit.plugin.java.JavaPlugin;
public class MenuManager implements Listener {
public static Inventory createMenu() {
Inventory menu = Bukkit.createInventory(null, 6 * 9, “Меню”);
ItemStack skillsItem = createItem(Material.IRON_SWORD, “Навыки”);
ItemMeta skillsMeta = skillsItem.getItemMeta();
skillsMeta.setUnbreakable(true);
skillsItem.setItemMeta(skillsMeta);
menu.setItem(14, skillsItem);
ItemStack statsItem = createItem(Material.COMPASS, “Статистика”);
ItemMeta statsMeta = statsItem.getItemMeta();
statsMeta.setUnbreakable(true);
statsItem.setItemMeta(statsMeta);
menu.setItem(4, statsItem);
return menu;
}
private static Inventory createSkillsMenu() {
Inventory skillsMenu = Bukkit.createInventory(null, 6 * 9, “Навыки”);
// Здесь добавьте предметы для меню “Навыки”
// skillsMenu.setItem(0, createItem(…))
// skillsMenu.setItem(1, createItem(…))
// …
return skillsMenu;
}
private static Inventory createStatsMenu(Player player) {
Inventory statsMenu = Bukkit.createInventory(null, 6 * 9, “Статистика”);
ItemStack healthItem = createItem(Material.REDSTONE_BLOCK, "Здоровье: " + player.getHealth());
ItemMeta healthMeta = healthItem.getItemMeta();
healthMeta.setUnbreakable(true);
healthItem.setItemMeta(healthMeta);
statsMenu.setItem(4, healthItem);
ItemStack swordItem = createItem(Material.IRON_SWORD, "Урон: " + player.getAttackDamage());
ItemMeta swordMeta = swordItem.getItemMeta();
swordMeta.setUnbreakable(true);
swordItem.setItemMeta(swordMeta);
statsMenu.setItem(24, swordItem);
ItemStack skinItem = createItem(Material.PAINTING, "Скин: " + player.getName());
ItemMeta skinMeta = skinItem.getItemMeta();
skinMeta.setUnbreakable(true);
skinItem.setItemMeta(skinMeta);
statsMenu.setItem(13, skinItem);
// Здесь добавьте остальные предметы для меню “Статистика”
// statsMenu.setItem(0, createItem(…))
// statsMenu.setItem(1, createItem(…))
// …
return statsMenu;
}
private static void openMenu(Player player) {
Inventory menu = createMenu();
player.openInventory(menu);
}
private static ItemStack createItem(Material material, String name) {
ItemStack item = new ItemStack(material);
ItemMeta meta = item.getItemMeta();
meta.setDisplayName(name);
item.setItemMeta(meta);
return item;
}
@EventHandler
public void onInventoryClick(InventoryClickEvent event) {
if (event.getClickedInventory() != null && event.getClickedInventory().equals(event.getWhoClicked().getInventory())) {
return;
}
if (event.getView().getTitle().equals(“Меню”)) {
event.setCancelled(true);
if (event.getSlot() == 4) {
Player player = (Player) event.getWhoClicked();
Inventory statsMenu = createStatsMenu(player);
player.openInventory(statsMenu);
}
} else if (event.getView().getTitle().equals(“Статистика”)) {
event.setCancelled(true);
}
// Другие проверки для остальных меню
}
}
public class MiningCore extends JavaPlugin {
@Override
public void onEnable() {
getServer().getPluginManager().registerEvents(new MenuManager(), this);
}
} почему не показывается урон в меню статистики в итоговом коде? скинь итоговый код
|
ed5e785a932fc92372ec719685b5fe7d
|
{
"intermediate": 0.3034631907939911,
"beginner": 0.45122501254081726,
"expert": 0.2453118860721588
}
|
32,337
|
document.querySelector("#topTD > div:nth-child(4) > h1").font-size = 50px;
VM1305:1 Uncaught SyntaxError: Invalid left-hand side in assignment
пытаюсь изменить цвет заголовка в редакторе кода в браузере, выдает ошибку
|
b1feaccbae89574941c279388ca8858c
|
{
"intermediate": 0.22807560861110687,
"beginner": 0.5884748101234436,
"expert": 0.1834496110677719
}
|
32,338
|
can you create a python code for object detection system using yolov5 model that detects from a camera source?
|
835858616107f3ba5acc0621a08f2388
|
{
"intermediate": 0.29248613119125366,
"beginner": 0.06030360981822014,
"expert": 0.6472102999687195
}
|
32,339
|
Переделай код, чтобы при вводе правильного логина и пароля пользователя переводило на страницу index.php document.addEventListener('DOMContentLoaded', function() {
document.querySelector('form').addEventListener('submit', function(e) {
e.preventDefault();
var login = document.getElementById('login').value;
var password = document.getElementById('password').value;
if (login && password) {
// Отправляем данные на сервер для проверки
var xhr = new XMLHttpRequest();
xhr.open('POST', 'log.php', true);
xhr.setRequestHeader('Content-type', 'application/x-www-form-urlencoded');
xhr.onreadystatechange = function() {
if (xhr.readyState === 4 && xhr.status === 200) {
var response = JSON.parse(xhr.responseText);
if (response.valid) {
// Если логин и пароль верные, перенаправляем на index.php
window.location = 'index.php';
} else {
// Если логин и пароль не верные, отображаем сообщение об ошибке
document.querySelector('.alert').textContent = 'Нет такого пользователя';
}
}
};
var data = 'login=' + login + '&pass=' + password;
xhr.send(data);
} else {
// В случае, если поля пустые, отображаем сообщение об ошибке
document.querySelector('.alert').textContent = 'Заполните все поля';
}
});
});
|
cd16def3f45c4c233c5e3d73f9b33570
|
{
"intermediate": 0.36222079396247864,
"beginner": 0.37832745909690857,
"expert": 0.2594517469406128
}
|
32,340
|
Сделай на js, чтобы когда пользователь ввёл правильный логин и пароль, его сразу бы перенаправило на страницу index.php <?php
require_once('db.php');
$login=$_POST['login'];
$pass=$_POST['pass'];
if (empty($login) || empty($pass))
{ "Заполните все поля"; } else {
$sql = "SELECT * FROM `users` WHERE login = '$login' AND pass = '$pass'";
$result = $conn->query($sql);
if ($result->num_rows > 0)
{ while($row = $result->fetch_assoc()){ $message = "Добро пожаловать " . $row['login']; }
} else { $message = "Нет такого пользователя"; } } ?>
|
4a1c909974afb94bba0b38d817dc23e6
|
{
"intermediate": 0.37233152985572815,
"beginner": 0.43756604194641113,
"expert": 0.1901024430990219
}
|
32,341
|
best 100% free text to video generators
|
872a7b958766b0b37592e90934a1e152
|
{
"intermediate": 0.3960147798061371,
"beginner": 0.299181193113327,
"expert": 0.3048039972782135
}
|
32,342
|
write .java code for aoh2 better combat system so if bigger army vs smaller then bigger army losses less
|
08ed4e022ea6b32ad2b2784db919db73
|
{
"intermediate": 0.41434890031814575,
"beginner": 0.16006475687026978,
"expert": 0.4255863130092621
}
|
32,343
|
add 25 more governments
{
Government: [
{
Name: "Nominal Democracy",
Extra_Tag: "",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.2,
MIN_GOODS: 0.14,
MIN_INVESTMENTS: 0.14,
RESEARCH_COST: 1.0,
INCOME_TAXATION: 1.0,
INCOME_PRODUCTION: 1.0,
MILITARY_UPKEEP: 1.0,
ADMINISTRATION_COST: 1.0,
ADMINISTRATION_COST_DISTANCE: 1.0,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 8,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 3,
COST_OF_RECRUIT: 18,
COST_OF_DISBAND: 16,
COST_OF_PLUNDER: 10,
DEFENSE_BONUS: 6,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 0,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 192,
G: 192,
B: 192
},
{
Name: "Monarchy",
Extra_Tag: "m",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.55,
MIN_GOODS: 0.2,
MIN_INVESTMENTS: 0.2,
RESEARCH_COST: 1.2,
INCOME_TAXATION: 1.25,
INCOME_PRODUCTION: 1.15,
MILITARY_UPKEEP: 1.1,
ADMINISTRATION_COST: 1.15,
ADMINISTRATION_COST_DISTANCE: 1.0,
ADMINISTRATION_COST_CAPITAL: 0.3,
COST_OF_MOVE: 8,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 2,
COST_OF_RECRUIT: 10,
COST_OF_DISBAND: 5,
COST_OF_PLUNDER: 8,
DEFENSE_BONUS: 8,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 0,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 255,
G: 194,
B: 36
},
{
Name: "Christian_Democracy",
Extra_Tag: "e",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.20,
MIN_GOODS: 0.21,
MIN_INVESTMENTS: 0.11,
RESEARCH_COST: 1.12,
INCOME_TAXATION: 1.16,
INCOME_PRODUCTION: 1.18,
MILITARY_UPKEEP: 1.17,
ADMINISTRATION_COST: 1.1,
ADMINISTRATION_COST_DISTANCE: 1.2,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 10,
COST_OF_MOVE_TO_THE_SAME_PROV: 5,
COST_OF_MOVE_OWN_PROV: 5,
COST_OF_RECRUIT: 15,
COST_OF_DISBAND: 15,
COST_OF_PLUNDER: 10,
DEFENSE_BONUS: 6,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 9,
REVOLUTIONARY: false,
AI_TYPE: "DEMOCRACY",
R: 225,
G: 226,
B: 229
},
{
Name: "Liberal",
Extra_Tag: "1",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.15,
MIN_GOODS: 0.15,
MIN_INVESTMENTS: 0.05,
RESEARCH_COST: 1.1,
INCOME_TAXATION: 0.85,
INCOME_PRODUCTION: 1.5,
MILITARY_UPKEEP: 1.25,
ADMINISTRATION_COST: 0.77,
ADMINISTRATION_COST_DISTANCE: 0.9,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 8,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 4,
COST_OF_RECRUIT: 20,
COST_OF_DISBAND: 18,
COST_OF_PLUNDER: 20,
DEFENSE_BONUS: 10,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 6,
REVOLUTIONARY: false,
AI_TYPE: "DEMOCRACY",
R: 35,
G: 87,
B: 169
},
{
Name: "Conservative",
Extra_Tag: "d",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.45,
MIN_GOODS: 0.25,
MIN_INVESTMENTS: 0.18,
RESEARCH_COST: 1.25,
INCOME_TAXATION: 1.35,
INCOME_PRODUCTION: 0.95,
MILITARY_UPKEEP: 0.85,
ADMINISTRATION_COST: 1.15,
ADMINISTRATION_COST_DISTANCE: 1.35,
ADMINISTRATION_COST_CAPITAL: 0.2,
COST_OF_MOVE: 8,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 1,
COST_OF_RECRUIT: 10,
COST_OF_DISBAND: 12,
COST_OF_PLUNDER: 8,
DEFENSE_BONUS: 7,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 6,
REVOLUTIONARY: false,
AI_TYPE: "DEMOCRACY",
R: 0,
G: 0,
B: 135
},
{
Name: "Social_Democrat",
Extra_Tag: "w",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.35,
MIN_GOODS: 0.35,
MIN_INVESTMENTS: 0.3,
RESEARCH_COST: 0.8,
INCOME_TAXATION: 1.35,
INCOME_PRODUCTION: 1.3,
MILITARY_UPKEEP: 1.25,
ADMINISTRATION_COST: 0.8,
ADMINISTRATION_COST_DISTANCE: 0.9,
ADMINISTRATION_COST_CAPITAL: 0.1,
COST_OF_MOVE: 10,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 1,
COST_OF_RECRUIT: 15,
COST_OF_DISBAND: 10,
COST_OF_PLUNDER: 15,
DEFENSE_BONUS: 5,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 8,
REVOLUTIONARY: false,
AI_TYPE: "DEMOCRACY",
R: 194,
G: 30,
B: 86
},
{
Name: "Authoritarian_Democracy",
Extra_Tag: "5",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.5,
MIN_GOODS: 0.15,
MIN_INVESTMENTS: 0.25,
RESEARCH_COST: 1.15,
INCOME_TAXATION: 1.4,
INCOME_PRODUCTION: 1.15,
MILITARY_UPKEEP: 1.35,
ADMINISTRATION_COST: 1.2,
ADMINISTRATION_COST_DISTANCE: 1.45,
ADMINISTRATION_COST_CAPITAL: 0.6,
COST_OF_MOVE: 6,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 2,
COST_OF_RECRUIT: 20,
COST_OF_DISBAND: 18,
COST_OF_PLUNDER: 5,
DEFENSE_BONUS: 1,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 7,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 110,
G: 110,
B: 110
},
{
Name: "Paternal_Autocrat",
Extra_Tag: "7",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.6,
MIN_GOODS: 0.2,
MIN_INVESTMENTS: 0.15,
RESEARCH_COST: 1.25,
INCOME_TAXATION: 1.35,
INCOME_PRODUCTION: 1.28,
MILITARY_UPKEEP: 0.85,
ADMINISTRATION_COST: 1.38,
ADMINISTRATION_COST_DISTANCE: 1.68,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 8,
COST_OF_MOVE_TO_THE_SAME_PROV: 6,
COST_OF_MOVE_OWN_PROV: 4,
COST_OF_RECRUIT: 10,
COST_OF_DISBAND: 18,
COST_OF_PLUNDER: 5,
DEFENSE_BONUS: 5,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 1,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 45,
G: 45,
B: 45
},
{
Name: "National_Populist",
Extra_Tag: "6",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.69,
MIN_GOODS: 0.2,
MIN_INVESTMENTS: 0.25,
RESEARCH_COST: 1.2,
INCOME_TAXATION: 1.35,
INCOME_PRODUCTION: 1.25,
MILITARY_UPKEEP: 0.8,
ADMINISTRATION_COST: 1.27,
ADMINISTRATION_COST_DISTANCE: 1.34,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 5,
COST_OF_MOVE_TO_THE_SAME_PROV: 3,
COST_OF_MOVE_OWN_PROV: 2,
COST_OF_RECRUIT: 15,
COST_OF_DISBAND: 18,
COST_OF_PLUNDER: 1,
DEFENSE_BONUS: 10,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 6,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 150,
G: 75,
B: 0
},
{
Name: "Autocracy",
Extra_Tag: "(",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.65,
MIN_GOODS: 0.19,
MIN_INVESTMENTS: 0.35,
RESEARCH_COST: 1.25,
INCOME_TAXATION: 1.25,
INCOME_PRODUCTION: 0.85,
MILITARY_UPKEEP: 1.15,
ADMINISTRATION_COST: 0.9,
ADMINISTRATION_COST_DISTANCE: 1.5,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 6,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 2,
COST_OF_RECRUIT: 15,
COST_OF_DISBAND: 10,
COST_OF_PLUNDER: 10,
DEFENSE_BONUS: 5,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 0,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 86,
G: 91,
B: 90
},
{
Name: "Green",
Extra_Tag: "&",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.42,
MIN_GOODS: 0.45,
MIN_INVESTMENTS: 0.15,
RESEARCH_COST: 0.9,
INCOME_TAXATION: 1.15,
INCOME_PRODUCTION: 0.8,
MILITARY_UPKEEP: 1.5,
ADMINISTRATION_COST: 0.65,
ADMINISTRATION_COST_DISTANCE: 0.65,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 22,
COST_OF_MOVE_TO_THE_SAME_PROV: 11,
COST_OF_MOVE_OWN_PROV: 2,
COST_OF_RECRUIT: 25,
COST_OF_DISBAND: 18,
COST_OF_PLUNDER: 10,
DEFENSE_BONUS: 10,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 10,
REVOLUTIONARY: false,
AI_TYPE: "DEMOCRACY",
R: 45,
G: 120,
B: 45
},
{
Name: "Reactionary",
Extra_Tag: "8",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.6,
MIN_GOODS: 0.25,
MIN_INVESTMENTS: 0.25,
RESEARCH_COST: 1.65,
INCOME_TAXATION: 1.35,
INCOME_PRODUCTION: 0.8,
MILITARY_UPKEEP: 1.15,
ADMINISTRATION_COST: 1.25,
ADMINISTRATION_COST_DISTANCE: 1.3,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 10,
COST_OF_MOVE_TO_THE_SAME_PROV: 8,
COST_OF_MOVE_OWN_PROV: 8,
COST_OF_RECRUIT: 20,
COST_OF_DISBAND: 18,
COST_OF_PLUNDER: 10,
DEFENSE_BONUS: 5,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 10,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 140,
G: 140,
B: 140
},
{
Name: "Valkism",
Extra_Tag: "%",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.67,
MIN_GOODS: 0.3,
MIN_INVESTMENTS: 0.35,
RESEARCH_COST: 0.75,
INCOME_TAXATION: 1.45,
INCOME_PRODUCTION: 1.25,
MILITARY_UPKEEP: 0.83,
ADMINISTRATION_COST: 1.27,
ADMINISTRATION_COST_DISTANCE: 1.3,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 4,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 2,
COST_OF_RECRUIT: 10,
COST_OF_DISBAND: 25,
COST_OF_PLUNDER: 1,
DEFENSE_BONUS: 11,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 9,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 45,
G: 45,
B: 45
},
{
Name: "Communism",
Extra_Tag: "c",
GOV_GROUP_ID: 1,
ACCEPTABLE_TAXATION: 0.8,
MIN_GOODS: 0.29,
MIN_INVESTMENTS: 0.35,
RESEARCH_COST: 0.65,
INCOME_TAXATION: 1.55,
INCOME_PRODUCTION: 1.6,
MILITARY_UPKEEP: 0.80,
ADMINISTRATION_COST: 1.2,
ADMINISTRATION_COST_DISTANCE: 1.4,
ADMINISTRATION_COST_CAPITAL: 0.5,
COST_OF_MOVE: 5,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 1,
COST_OF_RECRUIT: 10,
COST_OF_DISBAND: 25,
COST_OF_PLUNDER: 3,
DEFENSE_BONUS: 15,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 6,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 92,
G: 0,
B: 0
},
{
Name: "Syndicalism",
Extra_Tag: "2",
GOV_GROUP_ID: 1,
ACCEPTABLE_TAXATION: 0.55,
MIN_GOODS: 0.3,
MIN_INVESTMENTS: 0.25,
RESEARCH_COST: 1.05,
INCOME_TAXATION: 1.25,
INCOME_PRODUCTION: 1.5,
MILITARY_UPKEEP: 1.15,
ADMINISTRATION_COST: 1.25,
ADMINISTRATION_COST_DISTANCE: 1.5,
ADMINISTRATION_COST_CAPITAL: 0.5,
COST_OF_MOVE: 10,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 2,
COST_OF_RECRUIT: 13,
COST_OF_DISBAND: 10,
COST_OF_PLUNDER: 6,
DEFENSE_BONUS: 11,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 9,
REVOLUTIONARY: false,
AI_TYPE: "COMMUNISM",
R: 255,
G: 0,
B: 0
},
{
Name: "Socialism",
Extra_Tag: "q",
GOV_GROUP_ID: 1,
ACCEPTABLE_TAXATION: 0.4,
MIN_GOODS: 0.3,
MIN_INVESTMENTS: 0.15,
RESEARCH_COST: 0.85,
INCOME_TAXATION: 1.15,
INCOME_PRODUCTION: 1.35,
MILITARY_UPKEEP: 0.9,
ADMINISTRATION_COST: 1.25,
ADMINISTRATION_COST_DISTANCE: 1.35,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 10,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 4,
COST_OF_RECRUIT: 16,
COST_OF_DISBAND: 12,
COST_OF_PLUNDER: 6,
DEFENSE_BONUS: 3,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 6,
REVOLUTIONARY: false,
AI_TYPE: "COMMUNISM",
R: 115,
G: 0,
B: 150
},
{
Name: "Radical_Socialism",
Extra_Tag: "3",
GOV_GROUP_ID: 1,
ACCEPTABLE_TAXATION: 0.65,
MIN_GOODS: 0.25,
MIN_INVESTMENTS: 0.4,
RESEARCH_COST: 0.8,
INCOME_TAXATION: 0.8,
INCOME_PRODUCTION: 1.5,
MILITARY_UPKEEP: 0.90,
ADMINISTRATION_COST: 1.2,
ADMINISTRATION_COST_DISTANCE: 1.3,
ADMINISTRATION_COST_CAPITAL: 0.5,
COST_OF_MOVE: 10,
COST_OF_MOVE_TO_THE_SAME_PROV: 8,
COST_OF_MOVE_OWN_PROV: 5,
COST_OF_RECRUIT: 13,
COST_OF_DISBAND: 10,
COST_OF_PLUNDER: 6,
DEFENSE_BONUS: 7,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 9,
REVOLUTIONARY: false,
AI_TYPE: "COMMUNISM",
R: 233,
G: 58,
B: 58
},
{
Name: "Totalism",
Extra_Tag: "4",
GOV_GROUP_ID: 1,
ACCEPTABLE_TAXATION: 0.8,
MIN_GOODS: 0.25,
MIN_INVESTMENTS: 0.3,
RESEARCH_COST: 0.75,
INCOME_TAXATION: 0.8,
INCOME_PRODUCTION: 1.2,
MILITARY_UPKEEP: 0.85,
ADMINISTRATION_COST: 1.25,
ADMINISTRATION_COST_DISTANCE: 1.35,
ADMINISTRATION_COST_CAPITAL: 0.5,
COST_OF_MOVE: 5,
COST_OF_MOVE_TO_THE_SAME_PROV: 2,
COST_OF_MOVE_OWN_PROV: 2,
COST_OF_RECRUIT: 10,
COST_OF_DISBAND: 10,
COST_OF_PLUNDER: 2,
DEFENSE_BONUS: 10,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 9,
REVOLUTIONARY: false,
AI_TYPE: "COMMUNISM",
R: 127,
G: 0,
B: 0
},
{
Name: "Monarchism",
Extra_Tag: "}",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.55,
MIN_GOODS: 0.35,
MIN_INVESTMENTS: 0.15,
RESEARCH_COST: 1.45,
INCOME_TAXATION: 1.45,
INCOME_PRODUCTION: 1.25,
MILITARY_UPKEEP: 1.15,
ADMINISTRATION_COST: 1.15,
ADMINISTRATION_COST_DISTANCE: 1.2,
ADMINISTRATION_COST_CAPITAL: 0.1,
COST_OF_MOVE: 8,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 6,
COST_OF_RECRUIT: 15,
COST_OF_DISBAND: 12,
COST_OF_PLUNDER: 8,
DEFENSE_BONUS: 15,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 8,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 124,
G: 0,
B: 124
},
{
Name: "Fascism",
Extra_Tag: "f",
GOV_GROUP_ID: 2,
ACCEPTABLE_TAXATION: 0.5,
MIN_GOODS: 0.25,
MIN_INVESTMENTS: 0.3,
RESEARCH_COST: 0.90,
INCOME_TAXATION: 1.7,
INCOME_PRODUCTION: 1.5,
MILITARY_UPKEEP: 0.75,
ADMINISTRATION_COST: 1.3,
ADMINISTRATION_COST_DISTANCE: 1.45,
ADMINISTRATION_COST_CAPITAL: 0.6,
COST_OF_MOVE: 4,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 2,
COST_OF_RECRUIT: 10,
COST_OF_DISBAND: 10,
COST_OF_PLUNDER: 1,
DEFENSE_BONUS: 15,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 8,
REVOLUTIONARY: false,
AI_TYPE: "FASCISM",
R: 135,
G: 50,
B: 0
},
{
Name: "Nationalism",
Extra_Tag: "n",
GOV_GROUP_ID: 2,
ACCEPTABLE_TAXATION: 0.65,
MIN_GOODS: 0.4,
MIN_INVESTMENTS: 0.1,
RESEARCH_COST: 1.15,
INCOME_TAXATION: 1.45,
INCOME_PRODUCTION: 1.55,
MILITARY_UPKEEP: 0.80,
ADMINISTRATION_COST: 1.2,
ADMINISTRATION_COST_DISTANCE: 1.45,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 4,
COST_OF_MOVE_TO_THE_SAME_PROV: 3,
COST_OF_MOVE_OWN_PROV: 3,
COST_OF_RECRUIT: 8,
COST_OF_DISBAND: 5,
COST_OF_PLUNDER: 0.1,
DEFENSE_BONUS: 20,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 9,
REVOLUTIONARY: false,
AI_TYPE: "FASCISM",
R: 80,
G: 50,
B: 0
},
{
Name: "Ultranational_Socialism",
Extra_Tag: "!",
GOV_GROUP_ID: 2,
ACCEPTABLE_TAXATION: 0.75,
MIN_GOODS: 0.4,
MIN_INVESTMENTS: 0.2,
RESEARCH_COST: 1.35,
INCOME_TAXATION: 1.3,
INCOME_PRODUCTION: 1.1,
MILITARY_UPKEEP: 0.85,
ADMINISTRATION_COST: 1.4,
ADMINISTRATION_COST_DISTANCE: 1.7,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 3,
COST_OF_MOVE_TO_THE_SAME_PROV: 1,
COST_OF_MOVE_OWN_PROV: 1,
COST_OF_RECRUIT: 35,
COST_OF_DISBAND: 3,
COST_OF_PLUNDER: 1,
DEFENSE_BONUS: 20,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 10,
REVOLUTIONARY: false,
AI_TYPE: "FASCISM",
R: 47,
G: 79,
B: 79
},
{
Name: "Republic",
Extra_Tag: "r",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.5,
MIN_GOODS: 0.25,
MIN_INVESTMENTS: 0.35,
RESEARCH_COST: 1.15,
INCOME_TAXATION: 1.35,
INCOME_PRODUCTION: 1.15,
MILITARY_UPKEEP: 1.2,
ADMINISTRATION_COST: 0.85,
ADMINISTRATION_COST_DISTANCE: 1.1,
ADMINISTRATION_COST_CAPITAL: 0.1,
COST_OF_MOVE: 14,
COST_OF_MOVE_TO_THE_SAME_PROV: 8,
COST_OF_MOVE_OWN_PROV: 6,
COST_OF_RECRUIT: 18,
COST_OF_DISBAND: 16,
COST_OF_PLUNDER: 9,
DEFENSE_BONUS: 7,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 0,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 0,
G: 59,
B: 114
},
{
Name: "Radicalism",
Extra_Tag: ".",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.6,
MIN_GOODS: 0.12,
MIN_INVESTMENTS: 0.37,
RESEARCH_COST: 1.12,
INCOME_TAXATION: 0.85,
INCOME_PRODUCTION: 1.2,
MILITARY_UPKEEP: 0.85,
ADMINISTRATION_COST: 0.7,
ADMINISTRATION_COST_DISTANCE: 0.9,
ADMINISTRATION_COST_CAPITAL: 0.1,
COST_OF_MOVE: 14,
COST_OF_MOVE_TO_THE_SAME_PROV: 6,
COST_OF_MOVE_OWN_PROV: 6,
COST_OF_RECRUIT: 18,
COST_OF_DISBAND: 16,
COST_OF_PLUNDER: 9,
DEFENSE_BONUS: 7,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 8,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 166,
G: 90,
B: 90
},
{
Name: "Horde",
Extra_Tag: "h",
GOV_GROUP_ID: 2,
ACCEPTABLE_TAXATION: 0.47,
MIN_GOODS: 0.2,
MIN_INVESTMENTS: 0.25,
RESEARCH_COST: 1.55,
INCOME_TAXATION: 1.35,
INCOME_PRODUCTION: 0.65,
MILITARY_UPKEEP: 0.9,
ADMINISTRATION_COST: 1.6,
ADMINISTRATION_COST_DISTANCE: 1.7,
ADMINISTRATION_COST_CAPITAL: 0.8,
COST_OF_MOVE: 6,
COST_OF_MOVE_TO_THE_SAME_PROV: 1,
COST_OF_MOVE_OWN_PROV: 1,
COST_OF_RECRUIT: 5,
COST_OF_DISBAND: 14,
COST_OF_PLUNDER: 1,
DEFENSE_BONUS: 15,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 0,
REVOLUTIONARY: false,
AI_TYPE: "HORDE",
R: 28,
G: 99,
B: 75
},
{
Name: "CityState",
Extra_Tag: "s",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.3,
MIN_GOODS: 0.35,
MIN_INVESTMENTS: 0.1,
RESEARCH_COST: 1.2,
INCOME_TAXATION: 1.1,
INCOME_PRODUCTION: 1.15,
MILITARY_UPKEEP: 1.95,
ADMINISTRATION_COST: 0.85,
ADMINISTRATION_COST_DISTANCE: 8.25,
ADMINISTRATION_COST_CAPITAL: 0.025,
COST_OF_MOVE: 10,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 4,
COST_OF_RECRUIT: 25,
COST_OF_DISBAND: 19,
COST_OF_PLUNDER: 4,
DEFENSE_BONUS: 25,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 0,
REVOLUTIONARY: false,
AI_TYPE: "CITYSTATE",
R: 117,
G: 28,
B: 136
},
{
Name: "Tribal",
Extra_Tag: "t",
GOV_GROUP_ID: 3,
ACCEPTABLE_TAXATION: 0.30,
MIN_GOODS: 0.1,
MIN_INVESTMENTS: 0.06,
RESEARCH_COST: 0.4,
INCOME_TAXATION: 1.1,
INCOME_PRODUCTION: 1.00,
MILITARY_UPKEEP: 1.6,
ADMINISTRATION_COST: 1.175,
ADMINISTRATION_COST_DISTANCE: 9.15,
ADMINISTRATION_COST_CAPITAL: 0.01,
COST_OF_MOVE: 15,
COST_OF_MOVE_TO_THE_SAME_PROV: 8,
COST_OF_MOVE_OWN_PROV: 2,
COST_OF_RECRUIT: 16,
COST_OF_DISBAND: 16,
COST_OF_PLUNDER: 12,
DEFENSE_BONUS: 10,
CAN_BECOME_CIVILIZED: 1,
CIVILIZE_TECH_LEVEL: 0.25f,
AVAILABLE_SINCE_AGE_ID: 0,
REVOLUTIONARY: false,
AI_TYPE: "UNCIVILIZED",
R: 51,
G: 153,
B: 102
},
{
Name: "Rebels",
Extra_Tag: "u",
GOV_GROUP_ID: 4,
ACCEPTABLE_TAXATION: 0.24,
MIN_GOODS: 0.01,
MIN_INVESTMENTS: 0.1,
RESEARCH_COST: 8.0,
INCOME_TAXATION: 1.55,
INCOME_PRODUCTION: 0.95,
MILITARY_UPKEEP: 0.15,
ADMINISTRATION_COST: 0.7,
ADMINISTRATION_COST_DISTANCE: 1.00,
ADMINISTRATION_COST_CAPITAL: 0.1,
COST_OF_MOVE: 2,
COST_OF_MOVE_TO_THE_SAME_PROV: 2,
COST_OF_MOVE_OWN_PROV: 0,
COST_OF_RECRUIT: 1,
COST_OF_DISBAND: 2,
COST_OF_PLUNDER: 2,
DEFENSE_BONUS: 40,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 0,
REVOLUTIONARY: true,
AI_TYPE: "REBELS",
R: 92,
G: 92,
B: 92
},
{
Name: "Centrism",
Extra_Tag: "j",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.4,
MIN_GOODS: 0.2,
MIN_INVESTMENTS: 0.25,
RESEARCH_COST: 1.2,
INCOME_TAXATION: 1.65,
INCOME_PRODUCTION: 0.9,
MILITARY_UPKEEP: 1.15,
ADMINISTRATION_COST: 1.25,
ADMINISTRATION_COST_DISTANCE: 1.5,
ADMINISTRATION_COST_CAPITAL: 0.5,
COST_OF_MOVE: 8,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 6,
COST_OF_RECRUIT: 20,
COST_OF_DISBAND: 12,
COST_OF_PLUNDER: 10,
DEFENSE_BONUS: 7,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 10,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 0,
G: 127,
B: 127
},
{
Name: "Islamism",
Extra_Tag: "i",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.25,
MIN_GOODS: 0.15,
MIN_INVESTMENTS: 0.15,
RESEARCH_COST: 1.25,
INCOME_TAXATION: 1.25,
INCOME_PRODUCTION: 0.95,
MILITARY_UPKEEP: 1.24,
ADMINISTRATION_COST: 1.0,
ADMINISTRATION_COST_DISTANCE: 1.0,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 5,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 3,
COST_OF_RECRUIT: 10,
COST_OF_DISBAND: 8,
COST_OF_PLUNDER: 1,
DEFENSE_BONUS: 15,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 10,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 0,
G: 85,
B: 0
},
{
Name: "Salafist",
Extra_Tag: "+",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.45,
MIN_GOODS: 0.25,
MIN_INVESTMENTS: 0.18,
RESEARCH_COST: 1.35,
INCOME_TAXATION: 0.5,
INCOME_PRODUCTION: 1.35,
MILITARY_UPKEEP: 0.85,
ADMINISTRATION_COST: 1.1,
ADMINISTRATION_COST_DISTANCE: 1.2,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 5,
COST_OF_MOVE_TO_THE_SAME_PROV: 4,
COST_OF_MOVE_OWN_PROV: 3,
COST_OF_RECRUIT: 10,
COST_OF_DISBAND: 8,
COST_OF_PLUNDER: 1,
DEFENSE_BONUS: 17,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 10,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 35,
G: 35,
B: 35
},
{
Name: "Progressivism",
Extra_Tag: "y",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 1.0,
MIN_GOODS: 0.05,
MIN_INVESTMENTS: 0.35,
RESEARCH_COST: 0.65,
INCOME_TAXATION: 0.85,
INCOME_PRODUCTION: 1.12,
MILITARY_UPKEEP: 1.2,
ADMINISTRATION_COST: 1.25,
ADMINISTRATION_COST_DISTANCE: 1.4,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 25,
COST_OF_MOVE_TO_THE_SAME_PROV: 10,
COST_OF_MOVE_OWN_PROV: 8,
COST_OF_RECRUIT: 25,
COST_OF_DISBAND: 12,
COST_OF_PLUNDER: 5,
DEFENSE_BONUS: 5,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 11,
REVOLUTIONARY: false,
AI_TYPE: "DEMOCRACY",
R: 0,
G: 120,
B: 220
},
{
Name: "Libertharian_Socialism",
Extra_Tag: "{",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.34,
MIN_GOODS: 0.17,
MIN_INVESTMENTS: 0.17,
RESEARCH_COST: 1.28,
INCOME_TAXATION: 1.25,
INCOME_PRODUCTION: 0.90,
MILITARY_UPKEEP: 1.19,
ADMINISTRATION_COST: 1.15,
ADMINISTRATION_COST_DISTANCE: 1.2,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 6,
COST_OF_MOVE_TO_THE_SAME_PROV: 6,
COST_OF_MOVE_OWN_PROV: 3,
COST_OF_RECRUIT: 18,
COST_OF_DISBAND: 16,
COST_OF_PLUNDER: 12,
DEFENSE_BONUS: 10,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 8,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 146,
G: 0,
B: 0
},
{
Name: "Military_junta",
Extra_Tag: "p",
GOV_GROUP_ID: 2,
ACCEPTABLE_TAXATION: 0.55,
MIN_GOODS: 0.1,
MIN_INVESTMENTS: 0.33,
RESEARCH_COST: 1.2,
INCOME_TAXATION: 0.85,
INCOME_PRODUCTION: 1.1,
MILITARY_UPKEEP: 0.55,
ADMINISTRATION_COST: 1.25,
ADMINISTRATION_COST_DISTANCE: 1.55,
ADMINISTRATION_COST_CAPITAL: 0.6,
COST_OF_MOVE: 4,
COST_OF_MOVE_TO_THE_SAME_PROV: 2,
COST_OF_MOVE_OWN_PROV: 1,
COST_OF_RECRUIT: 12,
COST_OF_DISBAND: 10,
COST_OF_PLUNDER: 1,
DEFENSE_BONUS: 10,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 6,
REVOLUTIONARY: false,
AI_TYPE: "FASCISM",
R: 148,
G: 25,
B: 25
},
{
Name: "Oligarchy",
Extra_Tag: "o",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.85,
MIN_GOODS: 0.15,
MIN_INVESTMENTS: 0.45,
RESEARCH_COST: 1.4,
INCOME_TAXATION: 1.6,
INCOME_PRODUCTION: 1.4,
MILITARY_UPKEEP: 1.2,
ADMINISTRATION_COST: 1.15,
ADMINISTRATION_COST_DISTANCE: 1.35,
ADMINISTRATION_COST_CAPITAL: 0.2,
COST_OF_MOVE: 10,
COST_OF_MOVE_TO_THE_SAME_PROV: 8,
COST_OF_MOVE_OWN_PROV: 3,
COST_OF_RECRUIT: 17,
COST_OF_DISBAND: 10,
COST_OF_PLUNDER: 10,
DEFENSE_BONUS: 4,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 0,
REVOLUTIONARY: false,
AI_TYPE: "DEMOCRACY",
R: 200,
G: 172,
B: 97
},
{
Name: "Theocracy",
Extra_Tag: "g",
GOV_GROUP_ID: 0,
ACCEPTABLE_TAXATION: 0.32,
MIN_GOODS: 0.32,
MIN_INVESTMENTS: 0.05,
RESEARCH_COST: 1.25,
INCOME_TAXATION: 1.4,
INCOME_PRODUCTION: 0.57,
MILITARY_UPKEEP: 1.6,
ADMINISTRATION_COST: 1.2,
ADMINISTRATION_COST_DISTANCE: 1.2,
ADMINISTRATION_COST_CAPITAL: 0.4,
COST_OF_MOVE: 2,
COST_OF_MOVE_TO_THE_SAME_PROV: 2,
COST_OF_MOVE_OWN_PROV: 1,
COST_OF_RECRUIT: 40,
COST_OF_DISBAND: 12,
COST_OF_PLUNDER: 10,
DEFENSE_BONUS: 15,
CAN_BECOME_CIVILIZED: -1,
CIVILIZE_TECH_LEVEL: 2.0f,
AVAILABLE_SINCE_AGE_ID: 0,
REVOLUTIONARY: false,
AI_TYPE: "DEFAULT",
R: 112,
G: 112,
B: 112
},
],
Age_of_Civilizations: Governments
}
|
b63b4597272d6969af9f2366655d7cbd
|
{
"intermediate": 0.3349548578262329,
"beginner": 0.37850216031074524,
"expert": 0.28654298186302185
}
|
32,344
|
toObject(spectator = false) {
return { map_id: this.id, sound_id: this.sound, spectator: spectator, game_mode: this.game_mode, kick_period_ms: 300000, skybox_id: this.skybox_id, invisible_time: 3500, resources: };
}
}
exports.Map = Map;
как сюда добавить mounted_turret, mounted_hull, mounted_paint из const { market_list } = require("./server");
const GarageItem = require("./GarageItem"),
Area = require("./Area"),
{ turrets, hulls, paints, getType } = require("./server"),
InventoryItem = require("./InventoryItem");
class Garage extends Area {
static fromJSON(data) {
if (typeof (data.tank) !== "undefiend") {
var garage = new Garage(data.tank);
garage.items = Garage.fromItemsObject(data.items);
garage.updateMarket();
garage.mounted_turret = data.mounted_turret;
garage.mounted_hull = data.mounted_hull;
garage.mounted_paint = data.mounted_paint;
garage.inventory = null;
return garage;
}
return null;
}
static fromItemsObject(obj) {
var items = [];
for (var i in obj.items) {
items.push(GarageItem.fromJSON(obj.items[i]));
}
return items;
}
constructor(tank) {
super();
this.tank = tank;
this.items = [GarageItem.get("smoky", 0), GarageItem.get("green", 0), GarageItem.get("holiday", 0), GarageItem.get("wasp", 0), GarageItem.get("health", 0)];
this.updateMarket();
this.mounted_turret = "smoky_m0";
this.mounted_hull = "wasp_m0";
this.mounted_paint = "green_m0";
this.inventory = [];
}
getPrefix() {
return "garage";
}
getInventory() {
// if (this.inventory !== null)
// return this.inventory;
var inventory = [];
for (var i in this.items) {
var item = this.items[i];
if (getType(item.id) === 4) {
inventory.push(InventoryItem.fromGarageItem(item));
}
}
this.inventory = inventory;
return inventory;
}
addGarageItem(id, amount) {
var tank = this.tank;
var new_item = GarageItem.get(id, 0);
if (new_item !== null) {
var item = tank.garage.getItem(id);
if (item === null) {
new_item.count = amount;
this.addItem(new_item);
} else {
item.count += amount;
}
}
return true;
}
useItem(id) {
for (var i in this.items) {
if (this.items[i].isInventory && this.items[i].id === id) {
if (this.items[i].count <= 0)
return false;
if (this.items[i].count = 1)
{
this.deleteItem(this.items[i])
}
--this.items[i].count;
}
}
return true;
}
hasItem(id, m = null) {
for (var i in this.items) {
if (this.items[i].id === id) {
if (this.items[i].type !== 4) {
if (m === null)
return true;
return this.items[i].modificationID >= m;
}
else if (this.items[i].count > 0)
return true;
else {
this.items.splice(i, 1);
}
}
}
return false;
}
initiate(socket) {
this.send(socket, "init_garage_items;" + JSON.stringify({ items: this.items }));
this.addPlayer(this.tank);
}
initMarket(socket) {
this.send(socket, "init_market;" + JSON.stringify(this.market));
}
initMountedItems(socket) {
this.send(socket, "init_mounted_item;" + this.mounted_hull);
this.send(socket, "init_mounted_item;" + this.mounted_turret);
this.send(socket, "init_mounted_item;" + this.mounted_paint);
}
getItem(id) {
for (var i in this.items) {
if (this.items[i].id === id)
return this.items[i];
}
return null;
}
getTurret() {
return this.getItem(this.mounted_turret.split("_")[0]);
}
getHull() {
return this.getItem(this.mounted_hull.split("_")[0]);
}
getPaint() {
return this.getItem(this.mounted_paint.split("_")[0]);
}
updateMarket() {
this.market = { items: [] };
for (var i in market_list) {
if (!this.hasItem(market_list[i]["id"]) && market_list[i]["index"] >= 0)
{
this.market.items.push(market_list[i]);
}
this.hasItem(market_list[i]["multicounted"])
}
}
onData(socket, args) {
if (this.tank === null || !this.hasPlayer(this.tank.name))
return;
var tank = this.tank;
if (args.length === 1) {
if (args[0] === "get_garage_data") {
this.updateMarket();
setTimeout(() => {
this.initMarket(socket);
this.initMountedItems(socket);
}, 1500);
}
} else if (args.length === 3) {
if (args[0] === "try_buy_item") {
var itemStr = args[1];
var arr = itemStr.split("_");
var id = itemStr.replace("_m0", "");
var amount = parseInt(args[2]);
var new_item = GarageItem.get(id, 0);
if (new_item !== null) {
if (tank.crystals >= new_item.price * amount && tank.rank >= new_item.rank) {
var obj = {};
obj.itemId = id;
obj.multicounted = arr[2];
if (new_item !== null && id !== "1000_scores")
{
var item = tank.garage.getItem(id);
if (item === null)
{
new_item.count = amount;
this.addItem(new_item);
}
else
{
item.count += amount;
}
}
tank.crystals -= new_item.price * amount;
if (id === "1000_scores")
tank.addScore(1000 * amount);
if (id === "supplies")
{
this.addKitItem("health",0,100,tank);
this.addKitItem("armor",0,100,tank);
this.addKitItem("damage",0,100,tank);
this.addKitItem("nitro",0,100,tank);
this.addKitItem("mine",0,100,tank);
this.send(socket,"reload")
}
else
{
this.send(socket, "buy_item;" + itemStr + ";" + JSON.stringify(obj));
}
tank.sendCrystals();
}
}
}
} else if (args.length === 2) {
if (args[0] === "try_mount_item") {
var itemStr = args[1];
var itemStrArr = itemStr.split("_");
if (itemStrArr.length === 2) {
var modificationStr = itemStrArr[1];
var item_id = itemStrArr[0];
if (modificationStr.length === 2) {
var m = parseInt(modificationStr.charAt(1));
if (!isNaN(m)) {
this.mountItem(item_id, m);
this.sendMountedItem(socket, itemStr);
tank.save();
}
}
}
} else if (args[0] === "try_update_item") {
var itemStr = args[1],
arr = itemStr.split("_"),
modStr = arr.pop(),
id = arr.join("_");
if (modStr.length === 2) {
var m = parseInt(modStr.charAt(1));
if (!isNaN(m) && m < 3) {
if (this.hasItem(id, m) && this.getItem(id).attempt_upgrade(tank)) {
this.send(socket, "update_item;" + itemStr);
tank.sendCrystals();
}
}
}
}
}
}
addKitItem(item, m, count, tank) {
// this.items.push(item);
var itemStr = item + "_m"
var arr = itemStr.split("_");
var id = itemStr.replace("_m", "");
var amount = parseInt(count);
var new_item = GarageItem.get(id, 0);
console.log(new_item)
if (new_item !== null) {
var obj = {};
obj.itemId = id;
var item = tank.garage.getItem(id);
if (item === null) {
new_item.count = amount;
new_item.modificationID = m;
obj.count = amount;
obj.multicounted = item.multicounted;
this.addItem(new_item);
} else {
item.count += amount;
item.modificationID = m;
obj.count = item.count;
obj.multicounted = item.multicounted;
}
}
}
addItem(item) {
this.items.push(item);
}
deleteItem(item) {
delete this.items[item];
}
mountItem(item_id, m) {
if (this.hasItem(item_id, m)) {
var itemStr = item_id + "_m" + m;
if (hulls.includes(item_id))
this.mounted_hull = itemStr;
else if (turrets.includes(item_id))
this.mounted_turret = itemStr;
else if (paints.includes(item_id))
this.mounted_paint = itemStr;
}
}
sendMountedItem(socket, itemStr) {
this.send(socket, "mount_item;" + itemStr);
}
getItemsObject() {
var items = [];
for (var i in this.items) {
items.push(this.items[i].toObject());
}
return { items: items };
}
toSaveObject() {
return { items: this.getItemsObject(), mounted_turret: this.mounted_turret, mounted_hull: this.mounted_hull, mounted_paint: this.mounted_paint };
}
}
module.exports = Garage;
|
1b61710484edffecc130d2a5d937004c
|
{
"intermediate": 0.3561175465583801,
"beginner": 0.44245511293411255,
"expert": 0.2014273852109909
}
|
32,345
|
Write bootstrap code for navbar
Upper navbar
two selectors at top right
lower navbar
logo
products
claims
contact us
login button
It should be mobile responsive
|
381aec4dd6779ed83d39c6b5f7733913
|
{
"intermediate": 0.43588611483573914,
"beginner": 0.17750923335552216,
"expert": 0.3866046071052551
}
|
32,346
|
File "<ipython-input-16-da465df87554>", line 12
plt.figure(figsize=(10, 6))
^
IndentationError: expected an indented block after function definition on line 11
|
9faf006046906ad93d77339cee5e73b7
|
{
"intermediate": 0.3749421536922455,
"beginner": 0.3820054829120636,
"expert": 0.2430523782968521
}
|
32,347
|
ai generate using Linear Regression algorithm 6 main numbers and 5 lines with balanced selection of odd and even numbers, as well as a mix of low, mid, and high numbers main numbers 1 to 70 with one extra bonus number 1 to 25 with no duplicate bonus and main numbers on each line very important to exclude from main numbers line only the following numbers 5 12 21 23 49 50 51 60 65 67
|
5d3602f91c26568836674d28d5bce804
|
{
"intermediate": 0.1357109695672989,
"beginner": 0.060322754085063934,
"expert": 0.8039662837982178
}
|
32,348
|
XImage *pWindowBuffer = XCreateImage(pDisplay, visual, 24, ZPixmap, 0, pPixels, width, height, 32, 0);
XPutImage(pDisplay, window, gc, pWindowBuffer, 0, 0, 0, 0, width, height);
pPixels is an array of width * height * 3 uint8_ts. But when the image is displayed, I get the wrong colors. It sort of looks weird, as if it's reading more channels than it should be.
|
67af55cc4d5bb0c706abeef93fbec137
|
{
"intermediate": 0.4605601131916046,
"beginner": 0.21451541781425476,
"expert": 0.324924498796463
}
|
32,349
|
how whoosh library handles latex characters
|
c8ca015bef80e185f21baa64b4bb8a97
|
{
"intermediate": 0.7843217849731445,
"beginner": 0.1271672248840332,
"expert": 0.08851103484630585
}
|
32,350
|
Hi
|
5b8f2700936f1dbee61f12574d88297f
|
{
"intermediate": 0.33010533452033997,
"beginner": 0.26984941959381104,
"expert": 0.400045245885849
}
|
32,351
|
python code using BiLSTM encoder and BiLSTM decoder rnn and attention layer to translate English text to Arabic text using train, validate and test data from text files ,Tokenize the sentences and convert them into numerical representations ,Pad or truncate the sentences to a fixed length and use test data to evaluate and example prediction translation from test
|
c4e479dc089e9f2c5daedb2ae44f50e9
|
{
"intermediate": 0.3748818337917328,
"beginner": 0.09119625389575958,
"expert": 0.5339218974113464
}
|
32,352
|
How do I color icons in layout?
|
0a5183f2812093568ce11f9cdcc3ad7e
|
{
"intermediate": 0.3658815920352936,
"beginner": 0.355379581451416,
"expert": 0.2787388861179352
}
|
32,353
|
напиши плагин на майнкрафт спигот 1.12.2, сделай чтобы создавался файл stats.yml в котором записывался бы каждый игрок и его количество strength, сделай чтобы базовое количество strength было 0, сделай чтобы за каждую 1 единицу стренжа у игрока повышался урон на 1% и скинь итоговый код со всеми классами.
|
b920d7714241600b53d9239f7859cce4
|
{
"intermediate": 0.3143532872200012,
"beginner": 0.3134244680404663,
"expert": 0.37222224473953247
}
|
32,354
|
import numpy as np
from keras.models import Model
from keras.layers import Input, LSTM, Bidirectional, Concatenate, Dot,Embedding,Activation,Dense
from keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Define hyperparameters
max_sequence_length = 100
embedding_dim = 100
latent_dim = 256
# Load train, validate, and test data from text files
with open('D:/PHD Papers/scientific translation/GPT/First Code BiLSTM/NewResult/Train.txt', 'r', encoding='utf-8') as f:
train_data = f.read().splitlines()
with open('D:/PHD Papers/scientific translation/GPT/First Code BiLSTM/NewResult/Validate.txt', 'r', encoding='utf-8') as f:
validate_data = f.read().splitlines()
with open('D:/PHD Papers/scientific translation/GPT/First Code BiLSTM/NewResult/Test.txt', 'r', encoding='utf-8') as f:
test_data = f.read().splitlines()
# Tokenize sentences and convert them into numerical representations
tokenizer_en = Tokenizer()
tokenizer_en.fit_on_texts(train_data)
train_data_en = tokenizer_en.texts_to_sequences(train_data)
train_data_en = pad_sequences(train_data_en, maxlen=max_sequence_length)
tokenizer_ar = Tokenizer()
tokenizer_ar.fit_on_texts(train_data)
train_data_ar = tokenizer_ar.texts_to_sequences(train_data)
train_data_ar = pad_sequences(train_data_ar, maxlen=max_sequence_length)
# Define encoder inputs and LSTM layer
encoder_inputs = Input(shape=(max_sequence_length,))
encoder_embedding = Embedding(len(tokenizer_en.word_index) + 1, embedding_dim)(encoder_inputs)
encoder_lstm = Bidirectional(LSTM(latent_dim, return_sequences=True))(encoder_embedding)
# Define decoder inputs and LSTM layer
decoder_inputs = Input(shape=(max_sequence_length,))
decoder_embedding = Embedding(len(tokenizer_ar.word_index) + 1, embedding_dim)(decoder_inputs)
decoder_lstm = Bidirectional(LSTM(latent_dim, return_sequences=True))(decoder_embedding)
# Apply attention mechanism
attention = Dot(axes=[2, 2])([decoder_lstm, encoder_lstm])
attention = Activation('softmax')(attention)
context = Dot(axes=[2, 1])([attention, encoder_lstm])
decoder_combined_context = Concatenate(axis=-1)([context, decoder_lstm])
# Define decoder output layer
decoder_dense = Dense(len(tokenizer_ar.word_index) + 1, activation='softmax')
decoder_outputs = decoder_dense(decoder_combined_context)
# Define the model
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
# Train the model
model.fit([train_data_en, train_data_ar], np.expand_dims(train_data_ar, -1), batch_size=64, epochs=10,
validation_split=0.2)
# Use test data to evaluate the model
with open('D:/PHD Papers/scientific translation/GPT/First Code BiLSTM/NewResult/Test.txt', 'r', encoding='utf-8') as f:
test_data = f.read().splitlines()
test_data_en = tokenizer_en.texts_to_sequences(test_data)
test_data_en = pad_sequences(test_data_en, maxlen=max_sequence_length)
predicted_arabic = model.predict(test_data_en)
decoded_arabic = tokenizer_ar.sequences_to_texts(np.argmax(predicted_arabic, axis=-1))
# Print example prediction translation from test data
print("English sentence: ", test_data[0])
print("Predicted Arabic sentence: ", decoded_arabic[0])
|
44f65589cb95c287c638560ea367a235
|
{
"intermediate": 0.27769598364830017,
"beginner": 0.3991117477416992,
"expert": 0.3231922686100006
}
|
32,355
|
How can you write a java program to accept two user inputs
|
75593d5cb5d2648add1b909bad9aed81
|
{
"intermediate": 0.47428563237190247,
"beginner": 0.17439575493335724,
"expert": 0.3513186573982239
}
|
32,356
|
A ship leaves a port and sails 26 miles. Then the ship turns and travels another 62 miles due east. The angle between the ship’s path leaving the port and the line of sight from the port to the ship’s current location is 68 degrees. What is the distance, d,d, between the ship’s current location and the port? Round the answer to the nearest mile, if necessary.
|
7854f083a232afb0f84aa98a32f8cf0c
|
{
"intermediate": 0.3716689646244049,
"beginner": 0.29753053188323975,
"expert": 0.33080047369003296
}
|
32,357
|
Examine the following two relations which are part of a database for a company selling horse accessories.
Product
Product No
(PK) Description Price
P001 Riding Hat £50
P002 Jodhpurs £20
P003 Back Support £60
P004 Riding Boots £100
P005 Jacket £150
Warehouse
Warehouse No
(PK) City Address
W010 London 10 New Road
W020 Cambridge 23 Hills Road
W015 York 456 Long Road
W040 Bedford 45 Old Street
W025 Nottingham 56 Bar Hill
Assumptions:
1. The products sold by the company are stored in Warehouses.
2. Each warehouse can store many products.
3. Each product can be stored in many warehouses.
NOTE: The codes to create the above tables are given in the HorseDB.sql file. Download the file and execute it. You will need the Product and Warehouse tables before you create the Stock table:
Task 1: Create a Stock table which links the product table with the warehouse table and contains the attributes given in the table below. You are expected to give each attribute a relevant name and data type, and to correctly define the primary key and foreign keys:
Stock
Attribute Description Data Type
Attribute 1 Code used to uniquely identify the Stock. String – 5 characters
Attribute 2 Link to the Product Table. String – 5 characters
Attribute 3 Links to the Warehouse Table. String – 5 characters
Attribute 4 Stock quantity. Integer
Attribute 5 Aisle Number where stock located in warehouse. String – 5 characters
Attribute 6 Bin Number where stock is located in aisle. Integer
Task 2: Populate the table with at least 5 rows of data. Make sure the data is realistic and the Entity and Referential Integrity Constraint are not violated.
You will be marked on the following GOALs:
These GOALs are all individual and worth 1 mark.
I9.1. The correct use of the CREATE TABLE statement.
I9.2. Relevant attribute names.
I9.3 Relevant domain types.
I9.4. Correctly defined Primary Key.
I9.5 Correctly define the Foreign Keys.
I9.6. The correct use of the INSERT INTO statement.
I9.7. The correct use of the VALUES statement.
I9.8. Relevant data inserted into table.
I9.9. Demonstrate that the Entity Integrity constraint is not violated.
I9.10. Demonstrate that the Referential Integrity constraint is not violated.
|
dbffb109dbbef524687dac9fe19a01f6
|
{
"intermediate": 0.43948739767074585,
"beginner": 0.22957926988601685,
"expert": 0.3309333920478821
}
|
32,358
|
Give me an example of an RRN in javascript.
|
a2d1290d82f9f39e5b9b9f0f86363809
|
{
"intermediate": 0.23603542149066925,
"beginner": 0.20981433987617493,
"expert": 0.5541502833366394
}
|
32,359
|
def f_function(stream, key):
string = permute(stream, E)
for _ in string:
string = np.logical_xor(stream, key)
|
8a3d50726b7f571d1e6c9dc2ed0b28e8
|
{
"intermediate": 0.25866788625717163,
"beginner": 0.4828296899795532,
"expert": 0.25850242376327515
}
|
32,360
|
ecrire un programme qui calcule la somme d'un tableau initialisée par l'utilisateur de taille 5
|
4a0988fac26fc3644413a6e5584c7b9c
|
{
"intermediate": 0.15427526831626892,
"beginner": 0.056575093418359756,
"expert": 0.7891496419906616
}
|
32,361
|
write a java program on a scientific calculator
|
11c19098f69b8ab6f87e2a39690f3eb2
|
{
"intermediate": 0.3186849355697632,
"beginner": 0.3767813444137573,
"expert": 0.3045337200164795
}
|
32,362
|
for some reason, it doesn't generate wall tiles? it's only floor tiles:
extends TileMap
# Initialize noise generators for different map features
# Values between -1 and 1
var moisture = FastNoiseLite.new()
var temperature = FastNoiseLite.new()
var altitude = FastNoiseLite.new()
# Dimensions of each generated chunk
var width = 64
var height = 64
# Reference to the player character
@onready var player = get_tree().current_scene.get_node("Player")
# List to keep track of loaded chunks
var loaded_chunks = []
func _ready():
# Set random seeds for noise variation
moisture.seed = randi()
temperature.seed = randi()
altitude.seed = randi()
# Adjust this value to change the 'smoothness' of the map; lower values mean more smooth noise
altitude.frequency = 0.01
func _process(delta):
# Convert the player's position to tile coordinates
var player_tile_pos = local_to_map(player.position)
# Generate the chunk at the player's position
generate_chunk(player_tile_pos)
# Unload chunks that are too far away.
# Note: Not needed for smaller projects but if you are loading a bigger tilemap it's good practice
unload_distant_chunks(player_tile_pos)
func generate_chunk(pos):
for x in range(width):
for y in range(height):
# Generate noise values for moisture, temperature, and altitude
var moist = moisture.get_noise_2d(pos.x - (width/2) + x, pos.y - (height/2) + y) * 10 # Values between -10 and 10
var temp = temperature.get_noise_2d(pos.x - (width/2) + x, pos.y - (height/2) + y) * 10
var alt = altitude.get_noise_2d(pos.x - (width/2) + x, pos.y - (height/2) + y) * 10
# Set the cell based on altitude; adjust for different tile types
# Need to evenly distribute -10 -> 10 to 0 -> 4.... This can be done by first adding 10
# Gets values from 0 -> 20... Then we will multiply by 3/20 in order to remap it to 0 -> 3
# vvv
if alt < 0: # Arbitrary sea level value (choosing 0 will mean roughly 1/2 the world is ocean)
set_cell(0, Vector2i(pos.x - (width/2) + x, pos.y - (height/2) + y), 0, Vector2(3, round(3 * (temp + 10) / 20))) # Change x value where I've wrote three to whatever the x-coord of your oceans are
else: # You can add other logic like making beaches by setting x-coord to whatever beach atlas x-coord is when the alt is between 0 and 0.5 or something
set_cell(1, Vector2i(pos.x - (width/2) + x, pos.y - (height/2) + y), 0, Vector2(round(3 * (moist + 10) / 20), round(3 * (temp + 10) / 20)))
if Vector2i(pos.x, pos.y) not in loaded_chunks:
loaded_chunks.append(Vector2i(pos.x, pos.y))
# Function to unload chunks that are too far away
func unload_distant_chunks(player_pos):
# Set the distance threshold to at least 2 times the width to limit visual glitches
# Higher values unload chunks further away
var unload_distance_threshold = (width * 2) + 1
for chunk in loaded_chunks:
var distance_to_player = get_dist(chunk, player_pos)
if distance_to_player > unload_distance_threshold:
clear_chunk(chunk)
loaded_chunks.erase(chunk)
# Function to clear a chunk
func clear_chunk(pos):
for x in range(width):
for y in range(height):
set_cell(0, Vector2i(pos.x - (width/2) + x, pos.y - (height/2) + y), -1, Vector2(-1, -1), -1)
# Function to calculate distance between two points
func get_dist(p1, p2):
var resultant = p1 - p2
return sqrt(resultant.x ** 2 + resultant.y ** 2)
|
fb1b0c42a0782dbedd89f47c3f41f565
|
{
"intermediate": 0.3055081367492676,
"beginner": 0.3642495572566986,
"expert": 0.33024224638938904
}
|
32,363
|
C# play sound
|
56adf892290c079c51a2c5ab7dd0c5cb
|
{
"intermediate": 0.40023958683013916,
"beginner": 0.4543338119983673,
"expert": 0.14542663097381592
}
|
32,364
|
import numpy as np
from keras.models import Model
from keras.layers import Input, LSTM, Bidirectional, Concatenate, Dot,Embedding,Activation,Dense
from keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Define hyperparameters
max_sequence_length = 100
embedding_dim = 100
latent_dim = 256
# Load train, validate, and test data from text files
with open('D:/PHD Papers/scientific translation/GPT/First Code BiLSTM/NewResult/Train.txt', 'r', encoding='utf-8') as f:
train_data = f.read().splitlines()
with open('D:/PHD Papers/scientific translation/GPT/First Code BiLSTM/NewResult/Validate.txt', 'r', encoding='utf-8') as f:
validate_data = f.read().splitlines()
with open('D:/PHD Papers/scientific translation/GPT/First Code BiLSTM/NewResult/Test.txt', 'r', encoding='utf-8') as f:
test_data = f.read().splitlines()
# Tokenize sentences and convert them into numerical representations
tokenizer_en = Tokenizer()
tokenizer_en.fit_on_texts(train_data)
train_data_en = tokenizer_en.texts_to_sequences(train_data)
train_data_en = pad_sequences(train_data_en, maxlen=max_sequence_length)
tokenizer_ar = Tokenizer()
tokenizer_ar.fit_on_texts(train_data)
train_data_ar = tokenizer_ar.texts_to_sequences(train_data)
train_data_ar = pad_sequences(train_data_ar, maxlen=max_sequence_length)
# Define encoder inputs and LSTM layer
encoder_inputs = Input(shape=(max_sequence_length,))
encoder_embedding = Embedding(len(tokenizer_en.word_index) + 1, embedding_dim)(encoder_inputs)
encoder_lstm = Bidirectional(LSTM(latent_dim, return_sequences=True))(encoder_embedding)
# Define decoder inputs and LSTM layer
decoder_inputs = Input(shape=(max_sequence_length,))
decoder_embedding = Embedding(len(tokenizer_ar.word_index) + 1, embedding_dim)(decoder_inputs)
decoder_lstm = Bidirectional(LSTM(latent_dim, return_sequences=True))(decoder_embedding)
# Apply attention mechanism
attention = Dot(axes=[2, 2])([decoder_lstm, encoder_lstm])
attention = Activation('softmax')(attention)
context = Dot(axes=[2, 1])([attention, encoder_lstm])
decoder_combined_context = Concatenate(axis=-1)([context, decoder_lstm])
# Define decoder output layer
decoder_dense = Dense(len(tokenizer_ar.word_index) + 1, activation='softmax')
decoder_outputs = decoder_dense(decoder_combined_context)
# Define the model
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.summary()
# Train the model
model.fit([train_data_en, train_data_ar[:, :-1]], train_data_ar[:, 1:], batch_size=64, epochs=10,
validation_split=0.2)
# Use test data to evaluate the model
with open('D:/PHD Papers/scientific translation/GPT/First Code BiLSTM/NewResult/Test.txt', 'r', encoding='utf-8') as f:
test_data = f.read().splitlines()
test_data_en = tokenizer_en.texts_to_sequences(test_data)
test_data_en = pad_sequences(test_data_en, maxlen=max_sequence_length)
predicted_arabic = model.predict([test_data_en, np.zeros((test_data_en.shape[0], max_sequence_length))])
decoded_arabic = tokenizer_ar.sequences_to_texts(np.argmax(predicted_arabic, axis=-1))
# Print example prediction translation from test data
print("English sentence: ", test_data[0])
print("Predicted Arabic sentence: ", decoded_arabic[0])
|
b380518f93eade56cdbb0966efed2006
|
{
"intermediate": 0.29470062255859375,
"beginner": 0.3753960430622101,
"expert": 0.3299033045768738
}
|
32,365
|
C# soundplayer change volume
|
311099880d1b72b3812bcf2e77f52637
|
{
"intermediate": 0.378237783908844,
"beginner": 0.3154025077819824,
"expert": 0.3063597083091736
}
|
32,366
|
csr reply "Thank you for reach out to us!
I apologize but we had a hard time tracking your order from the details you provided.
We might need to have the last 4 digits of the card and what type of card was used"
please enhance this but make it simple,short, and concise
|
e1f16c0ab6f6f2c80ddc99a0a2181391
|
{
"intermediate": 0.32384490966796875,
"beginner": 0.29298117756843567,
"expert": 0.38317394256591797
}
|
32,367
|
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
public class Arrow : MonoBehaviour
{
void Start()
{
Invoke("Despawn", 3);
}
void Despawn()
{
Destroy(gameObject);
}
}
make it so it does 1 damage to an enemy if it collides with it
|
79ba43191ac00d15a5593fade90515d6
|
{
"intermediate": 0.3426853120326996,
"beginner": 0.5032713413238525,
"expert": 0.1540432870388031
}
|
32,368
|
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
public class BowAttack : MonoBehaviour
{
public float arrowSpeed = 1;
public GameObject arrowPrefab;
Rigidbody2D rigid;
void Start()
{
rigid = GetComponent<Rigidbody2D>();
}
void Update()
{
//Fire when we click the mouse
if (Input.GetMouseButtonDown(0))
{
//Get the vector from the player to the mouse
Vector3 mousePos = Input.mousePosition;
mousePos.z = 10;
Vector3 worldPos = Camera.main.ScreenToWorldPoint(mousePos);
Vector3 p2m = worldPos - transform.position;
p2m.Normalize();
//Spawn a bullet
GameObject go = Instantiate<GameObject>(arrowPrefab);
go.transform.position = transform.position;
//Shoot it in that direction
Rigidbody2D arrowRigid = go.GetComponent<Rigidbody2D>();
arrowRigid.velocity = p2m * arrowSpeed;
}
}
}
make it so that the arrow can only be fired every 1 seconds
|
c397954ad689e83742731db4cae3ddb1
|
{
"intermediate": 0.45203542709350586,
"beginner": 0.3275100588798523,
"expert": 0.22045446932315826
}
|
32,369
|
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
public class Chest : MonoBehaviour
{
public float delayTime = 5f;
public GameObject bowPrefab;
private void OnTriggerEnter2D(Collider2D collision)
{
if (collision.CompareTag("Player"))
{
// Generate a random number (between 0 and 1) to determine if bow spawns or not
float randomValue = Random.value;
if (randomValue <= 0.5f)
{
// Spawn a bow
GameObject bow = Instantiate(bowPrefab, collision.transform);
bow.transform.localPosition = new Vector3(-0.25f, -0.25f, 0f);
bow.transform.localRotation = Quaternion.identity;
}
// Disable the chest to prevent spawning multiple bows
gameObject.SetActive(false);
// Enable the chest after a delay
Invoke("EnableChest", delayTime);
}
}
private void EnableChest()
{
gameObject.SetActive(true);
}
} make it so the bow may only be instantiated once
|
bee52c7374e2c6b8efd69b5752ead049
|
{
"intermediate": 0.4746066629886627,
"beginner": 0.31191495060920715,
"expert": 0.21347838640213013
}
|
32,370
|
Your computer might have been infected by a virus! Create a python function that finds the viruses in files and removes them from your computer.
Examples
remove_virus("PC Files: spotifysetup.exe, virus.exe, dog.jpg") ➞ "PC Files: spotifysetup.exe, dog.jpg"
remove_virus("PC Files: antivirus.exe, cat.pdf, lethalmalware.exe, dangerousvirus.exe ") ➞ "PC Files: antivirus.exe, cat.pdf"
remove_virus("PC Files: notvirus.exe, funnycat.gif") ➞ "PC Files: notvirus.exe, funnycat.gif")
Notes
Bad files will contain "virus" or "malware", but "antivirus" and "notvirus" will not be viruses.
Return "PC Files: Empty" if there are no files left on the computer.
|
49abd0ec813edae1370cd0ffe12122fb
|
{
"intermediate": 0.3072793483734131,
"beginner": 0.3511654734611511,
"expert": 0.34155523777008057
}
|
32,371
|
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
public class Chest : MonoBehaviour
{
public float delayTime = 5f;
public GameObject bowPrefab;
public GameObject healthPickupPrefab;
private bool isBowSpawned = false;
private void OnTriggerEnter2D(Collider2D collision)
{
if (!isHealthPickupSpawned && collision.CompareTag("Player"))
{
float randomValue2 = Random.value;
if (randomValue2 <= 0.3f)
{
// Spawn a HealthPickup
GameObject healthPickup = Instantiate(healthPickupPrefab, collision.transform);
healthPickup.tag = "Health";
}
}
if (!isBowSpawned && collision.CompareTag("Player") && !IsBowAlreadySpawned())
{
// Generate a random number (between 0 and 1) to determine if bow spawns or not
float randomValue = Random.value;
if (randomValue <= 0.5f)
{
// Spawn a bow
GameObject bow = Instantiate(bowPrefab, collision.transform);
bow.transform.localPosition = new Vector3(-0.25f, -0.25f, 0f);
bow.transform.localRotation = Quaternion.identity;
bow.tag = "Bow"; // Set the tag for the spawned bow
isBowSpawned = true;
}
// Disable the chest to prevent spawning multiple bows
gameObject.SetActive(false);
// Enable the chest after a delay
Invoke("EnableChest", delayTime);
}
}
private bool IsBowAlreadySpawned()
{
GameObject[] bows = GameObject.FindGameObjectsWithTag("Bow");
return bows.Length > 0;
}
private void EnableChest()
{
gameObject.SetActive(true);
isBowSpawned = false;
}
} isHeatlhPickupSpawned has an error, what is wrong with it?
|
60796cafb9a2529e3a22fbd6bb2a4b7e
|
{
"intermediate": 0.5397460460662842,
"beginner": 0.2633788287639618,
"expert": 0.19687505066394806
}
|
32,372
|
Hi
|
902a2a155ec405507764b43b76c4ea73
|
{
"intermediate": 0.33010533452033997,
"beginner": 0.26984941959381104,
"expert": 0.400045245885849
}
|
32,373
|
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
public class Chest : MonoBehaviour
{
public float delayTime = 5f;
public GameObject bowPrefab;
public GameObject healthPickupPrefab;
private bool isBowSpawned = false;
bool isHealthPickupSpawned = false;
private void OnTriggerEnter2D(Collider2D collision)
{
if (!isHealthPickupSpawned && collision.CompareTag("Player"))
{
float randomValue2 = Random.value;
if (randomValue2 <= 0.3f)
{
// Spawn a HealthPickup
GameObject healthPickup = Instantiate(healthPickupPrefab, collision.transform);
healthPickup.transform.localPosition = new Vector3(0f, 0f, 0f);
healthPickup.transform.localRotation = Quaternion.identity;
healthPickup.tag = "Health";
}
}
if (!isBowSpawned && collision.CompareTag("Player") && !IsBowAlreadySpawned())
{
// Generate a random number (between 0 and 1) to determine if bow spawns or not
float randomValue = Random.value;
if (randomValue <= 0.5f)
{
// Spawn a bow
GameObject bow = Instantiate(bowPrefab, collision.transform);
bow.transform.localPosition = new Vector3(-0.25f, -0.25f, 0f);
bow.transform.localRotation = Quaternion.identity;
bow.tag = "Bow"; // Set the tag for the spawned bow
isBowSpawned = true;
}
// Disable the chest to prevent spawning multiple bows
gameObject.SetActive(false);
// Enable the chest after a delay
Invoke("EnableChest", delayTime);
}
}
private bool IsBowAlreadySpawned()
{
GameObject[] bows = GameObject.FindGameObjectsWithTag("Bow");
return bows.Length > 0;
}
private void EnableChest()
{
gameObject.SetActive(true);
isBowSpawned = false;
}
} after the bow is spawned, the chest are no longer being temporarily disabled
|
e6a779d61659ab9e00fca4570a52c23a
|
{
"intermediate": 0.31629031896591187,
"beginner": 0.4791385531425476,
"expert": 0.20457112789154053
}
|
32,374
|
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.SceneManagement;
public class Player : MonoBehaviour
{
public float speed = 5f; // Speed of character movement
public int maxHealth = 3; // Maximum player health
public int currentHealth; // Current player health
private Animator anim;
private void Start()
{
anim = GetComponent<Animator>();
currentHealth = maxHealth; // Set the initial health to maxHealth value
}
private void Update()
{
// Move character based on input axes
float horizontalInput = Input.GetAxis("Horizontal");
float verticalInput = Input.GetAxis("Vertical");
// Calculate movement direction
Vector2 movement = new Vector2(horizontalInput, verticalInput) * speed * Time.deltaTime;
// Apply movement to the character’s position
transform.Translate(movement);
anim.SetFloat("moveX", movement.x);
anim.SetFloat("moveY", movement.y);
}
private void OnCollisionEnter2D(Collision2D collision)
{
// Check if the player collides with an enemy
if (collision.gameObject.CompareTag("Enemy"))
{
TakeDamage(1); // Deduct 1 health point
if (currentHealth <= 0)
{
ResetScene(); // Reset the scene if health reaches 0
}
}
}
private void TakeDamage(int amount)
{
currentHealth -= amount; // Deduct the specified amount from the current health
}
public void AddHealth(int amount)
{
currentHealth += amount; // Increase the current health by the specified amount
currentHealth = Mathf.Min(currentHealth, maxHealth); // Ensure current health doesn’t exceed max health
}
private void ResetScene()
{
SceneManager.LoadScene(SceneManager.GetActiveScene().name); // Reload the current scene
}
}
make it so that if the player collides with a game object tagged "Health" they will get one health
|
0be76e283146f175518fa993b665d1a6
|
{
"intermediate": 0.43714460730552673,
"beginner": 0.3057388961315155,
"expert": 0.25711652636528015
}
|
32,375
|
What is 3x+y=27?
|
3a5be32d5ce77ed99eb060eedb685c4c
|
{
"intermediate": 0.25922879576683044,
"beginner": 0.2875294089317322,
"expert": 0.4532417356967926
}
|
32,376
|
Создай класс в html: <input type="submit" name="search" class="btn btn-primary" value="Поиск">
|
5e4b53be85ea68f2b6491edaceb9d145
|
{
"intermediate": 0.2798740863800049,
"beginner": 0.2893314063549042,
"expert": 0.43079447746276855
}
|
32,377
|
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
public class BossHealth : MonoBehaviour
{
public int maxHealth = 5;
private int currentHealth;
private void Start()
{
currentHealth = maxHealth;
}
public void TakeDamage(int damageAmount)
{
currentHealth -= damageAmount;
if (currentHealth <= 0)
{
Die();
}
}
private void Die()
{
// Destroy the enemy object when health reaches 0
Destroy(gameObject);
}
}
make it so if the health reaches 0, the scene changes to Level2
|
c8b2bfa8bea821ac4707fde4655f3a77
|
{
"intermediate": 0.382095605134964,
"beginner": 0.39684534072875977,
"expert": 0.22105903923511505
}
|
32,378
|
hi
|
11ba8ca18b5442e214c52754537e49a0
|
{
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
}
|
32,379
|
explain why OSPF requires ‘designated routers’
|
ba1707281bdf4dde969130a80eccb1b2
|
{
"intermediate": 0.4727025628089905,
"beginner": 0.2085961401462555,
"expert": 0.31870126724243164
}
|
32,380
|
find the longest palindrome sequence using python
|
52c702109a31a7076ae9a07e03dbed12
|
{
"intermediate": 0.22822511196136475,
"beginner": 0.1622800976037979,
"expert": 0.6094948053359985
}
|
32,381
|
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
public class Boss : MonoBehaviour
{
public float speed = 2f;
public float chaseRange = 5f;
private Transform player;
private Vector2 targetPosition;
private Rigidbody2D rb;
private void Start()
{
rb = GetComponent<Rigidbody2D>();
player = GameObject.FindGameObjectWithTag("Player").transform;
// Set initial target position as the current position
targetPosition = rb.position;
}
private void Update()
{
// Calculate the distance to the player
float distanceToPlayer = Vector2.Distance(rb.position, player.position);
// If the player is within chase range, set player’s position as the target
if (distanceToPlayer < chaseRange)
{
targetPosition = player.position;
}
else
{
// If the enemy reached the target position, set a new random target position
float distanceToTarget = Vector2.Distance(rb.position, targetPosition);
if (distanceToTarget < 0.1f)
{
targetPosition = GetRandomPosition();
}
}
// Move towards the target position
Vector2 direction = (targetPosition - rb.position).normalized;
rb.MovePosition(rb.position + direction * speed * Time.deltaTime);
}
private Vector2 GetRandomPosition()
{
// Get a random position within the map boundaries
float x = Random.Range(-5f, 5f);
float y = Random.Range(-5f, 5f);
return new Vector2(x, y);
}
}
Make it so the boss is only spawned in (at the point (0f, 0f, 0f,) after 10 enemy game objects are destroyed
|
c01083adeca9d7e547b433c68a9049cc
|
{
"intermediate": 0.4057077467441559,
"beginner": 0.32648083567619324,
"expert": 0.2678114175796509
}
|
32,382
|
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.SceneManagement;
public class Player : MonoBehaviour
{
public float speed = 5f; // Speed of character movement
public int maxHealth = 3; // Maximum player health
public int currentHealth; // Current player health
private Animator anim;
private void Start()
{
anim = GetComponent<Animator>();
currentHealth = maxHealth; // Set the initial health to maxHealth value
}
private void Update()
{
// Move character based on input axes
float horizontalInput = Input.GetAxis("Horizontal");
float verticalInput = Input.GetAxis("Vertical");
// Calculate movement direction
Vector2 movement = new Vector2(horizontalInput, verticalInput) * speed * Time.deltaTime;
// Apply movement to the character’s position
transform.Translate(movement);
anim.SetFloat("moveX", movement.x);
anim.SetFloat("moveY", movement.y);
}
private void OnCollisionEnter2D(Collision2D collision)
{
// Check if the player collides with an enemy
if (collision.gameObject.CompareTag("Enemy"))
{
TakeDamage(1); // Deduct 1 health point
if (currentHealth <= 0)
{
ResetScene(); // Reset the scene if health reaches 0
}
}
// Check if the player collides with a Boss
if (collision.gameObject.CompareTag("Boss"))
{
TakeDamage(1); // Deduct 1 health point
if (currentHealth <= 0)
{
ResetScene(); // Reset the scene if health reaches 0
}
}
// Check if the player collides with a health object
if (collision.gameObject.CompareTag("Health"))
{
AddHealth(1); // Increase health by 1
Destroy(collision.gameObject); // Destroy the health object
}
}
private void TakeDamage(int amount)
{
currentHealth -= amount; // Deduct the specified amount from the current health
}
public void AddHealth(int amount)
{
currentHealth += amount; // Increase the current health by the specified amount
currentHealth = Mathf.Min(currentHealth, maxHealth); // Ensure current health doesn’t exceed max health
}
private void ResetScene()
{
SceneManager.LoadScene(SceneManager.GetActiveScene().name); // Reload the current scene
}
}
make it so after the player takes damage, he is invincible for 1 second
|
bb5bb855ff53b67925abda86de6af3a3
|
{
"intermediate": 0.290312260389328,
"beginner": 0.4213525652885437,
"expert": 0.2883351445198059
}
|
32,383
|
Check this function:
fn build_gtf_line(
record: &BedRecord,
gene_name: &str,
gene_type: &str,
exon_start: u32,
exon_end: u32,
frame: u32,
exon: i16,
file: &mut File,
) {
assert!(record.tx_start() < record.tx_end());
let phase = if frame < 0 {
"."
} else if frame == 0 {
"0"
} else if frame == 1 {
"2"
} else {
"1"
};
let mut gtf_line = format!(
"{}\t{}\t{}\t{}\t{}\t.\t{}\t{}\t",
record.chrom(),
SOURCE,
gene_type,
exon_start + 1,
exon_end,
record.strand(),
phase,
);
gtf_line += &format!("gene_id \"{}\"; ", gene_name);
gtf_line += &format!("transcript_id \"{}\";", record.name());
if exon >= 0 {
if record.strand() == "-" {
gtf_line += &format!(" exon_number \"{}\";", record.exon_count() - exon);
gtf_line += &format!(
" exon_id \"{}.{}\";",
record.name(),
record.exon_count() - exon
);
} else {
gtf_line += &format!(" exon_number \"{}\";", exon + 1);
gtf_line += &format!(" exon_id \"{}.{}\";", record.name(), exon + 1);
}
}
gtf_line += "\n";
let _ = file.write_all(gtf_line.as_bytes());
}
Review it and correct any error. Re-implement anything you want to make this the most efficient and fastest way. Since you are a Rust expert I believe you will do it perfectly! Some notes to take into account are: the final line will not be written to a file but append it to a vector.
|
357e5d40485df78ead6d6a4c7669d7ad
|
{
"intermediate": 0.4590393602848053,
"beginner": 0.31552621722221375,
"expert": 0.22543449699878693
}
|
32,384
|
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
public class Boss : MonoBehaviour
{
public float speed = 2f;
public float chaseRange = 5f;
private Transform player;
private Vector2 targetPosition;
private Rigidbody2D rb;
private void Start()
{
rb = GetComponent<Rigidbody2D>();
player = GameObject.FindGameObjectWithTag("Player").transform;
// Set initial target position as the current position
targetPosition = rb.position;
}
private void Update()
{
// Calculate the distance to the player
float distanceToPlayer = Vector2.Distance(rb.position, player.position);
// If the player is within chase range, set player’s position as the target
if (distanceToPlayer < chaseRange)
{
targetPosition = player.position;
}
else
{
// If the enemy reached the target position, set a new random target position
float distanceToTarget = Vector2.Distance(rb.position, targetPosition);
if (distanceToTarget < 0.1f)
{
targetPosition = GetRandomPosition();
}
}
// Move towards the target position
Vector2 direction = (targetPosition - rb.position).normalized;
rb.MovePosition(rb.position + direction * speed * Time.deltaTime);
}
private Vector2 GetRandomPosition()
{
// Get a random position within the map boundaries
float x = Random.Range(-5f, 5f);
float y = Random.Range(-5f, 5f);
return new Vector2(x, y);
}
}
make it so the boss will shoot MagicBullets at the player every 2 seconds
|
fea8da20f22713dddc15fc69e4583ba7
|
{
"intermediate": 0.48455101251602173,
"beginner": 0.2761479616165161,
"expert": 0.23930108547210693
}
|
32,385
|
Есть недописанная программа реализации АВЛ-дерева. Допиши её, при следующих условиях: при удалении элемента программа заменяет его на максимальный слева; обход справа налево; добавить в дерево элементы 9, 1, 2, 2, 0, 9, 0, 3, 0, 4 и вывести его. Вот программа: #include <iostream>
using namespace std;
struct Node
{
int key, count;
Node *left, *right;
int balance;
};
void search(int x, Node* p, short h)
{
Node* p1, * p2;
if (p == NULL) // слова нет в дереве; включить его
{
p = new Node;
p->key = x;
p->balance = 0;
p->left = NULL;
p->right = NULL;
}
else if (x < p->key)
{
search(x, p->left, h);
if (h != 0) // выросла левая ветвь
{
switch (p->balance)
{
case 1:
p->balance = 0;
h = 0;
break;
case 0:
p->balance = -1;
break;
case -1: // балансировка
p1 = p->left;
if (p1->balance == -1) // L-поворот
{
p->left = p->right;
p1->right = p;
p->balance = 0;
p = p1;
}
else // LR-поворот
{
p2 = p1->right;
p1->right = p2->left;
p2->left = p1;
p->left = p2->right;
p2->right = p;
if (p2->balance == -1)
p->balance = 1;
else
p->balance = 0;
if (p2->balance == 1)
p1->balance = -1;
else
p1->balance = 0;
p = p2;
}
p->balance = 0;
h = 0;
break;
}
}
}
else if (x > p->key)
{
search(x, p->right, h);
if (h != 0) // выросла правая ветвь
{
switch (p->balance)
{
case -1:
p->balance = 0;
h = false;
break;
case 0:
p->balance = 1;
break;
case 1: // балансировка
p1 = p->right;
if (p1->balance == 1) //R-поворот
{
p->right = p1->left;
p1->left = p;
p->balance = 0;
p = p1;
}
else // RL-поворот
{
p2 = p1->left;
p1->left = p2->right;
p2->right = p1;
p->right = p2->left;
p2->left = p;
if (p2->balance == 1)
p->balance = -1;
else
p->balance = 0;
if (p2->balance == -1)
p1->balance = 1;
else
p1->balance = 0;
p = p2;
}
p->balance = 0;
h = 0;
break;
}
}
}
else
{
p->count += 1;
h = 0;
}
}
void balance1(Node* p, short h)
{
Node* p1, * p2;
short b1, b2;
switch (p->balance)
{
case -1:
p->balance = 0;
break;
case 0:
p->balance = 1;
h = 0;
break;
case 1: // балансировка
p1 = p->right;
b1 = p1->balance;
if (b1 >= 0) // R-поворот
{
p->right = p1->left;
p1->left = p;
if (b1 == 0)
{
p->balance = 1;
p1->balance = -1;
h = 0;
}
else
{
p->balance = 0;
p1->balance = 0;
}
p = p1;
}
else // RL-поворот
{
p2 = p1->left;
b2 = p2->balance;
p1->left = p2->right;
p2->right = p1;
p->right = p2->left;
p2->left = p;
if (b2 == 1)
p->balance = -1;
else
p->balance = 0;
if (b2 == -1)
p1->balance = 1;
else
p1->balance = 0;
p = p2;
p2->balance = 0;
}
}
}
void balance2(Node* p, short h)
{
Node* p1, * p2;
short b1, b2;
switch (p->balance)
{
case 1:
p->balance = 0;
break;
case 0:
p->balance = 0;
h = 0;
break;
case -1:
p1 = p->left;
b1 = p1->balance;
if (b1 <= 0) // L-поворот
{
p->left = p1->right;
p1->right = p;
if (b1 == 0)
{
p->balance = -1;
p1->balance = 1;
h = 0;
}
else
{
p->balance = 0;
p1->balance = 0;
}
p = p1;
}
else // LR-поворот
{
p2 = p1->right;
b2 = p2->balance;
p1->right = p2->left;
p2->left = p1;
p->left = p2->right;
p2->right = p;
if (b2 == -1)
p->balance = 1;
else
p->balance = 0;
if (b2 == 1)
p1->balance = -1;
else
p1->balance = 0;
p = p2;
p2->balance = 0;
}
}
}
void del(Node* r, Node* q, short h)
{
if (r->right != NULL)
{
del(r->right, q, h);
if (h != 0)
balance2(r, h);
}
else
{
q->key = r->key;
q->count = r->count;
r = r->left;
h = 1;
}
}
void delet(int x, Node* p, short h)
{
Node* q;
if (p == NULL)
{
cout << "Key is not in tree" << endl;
h = 0;
}
else if (x < p->key)
{
delet(x, p->left, h);
if (h != 0)
balance1(p, h);
}
else if (x > p->key)
{
delet(x, p->right, h);
if (h != 0)
balance2(p, h);
}
else
{
q = p;
if (q->right == NULL)
{
p = q->left;
h = 1;
}
else if (q->left == NULL)
{
p = q->right;
h = 1;
}
else
{
del(q->left, q, h);
if (h != 0)
balance1(p, h);
}
}
}
Node* tree(int x)
{
}
void printtree(Node* t, short h)
{
if (t != NULL)
{
printtree(t->left, h + 1);
for (int i = 1; i <= h; i++)
{
cout << ' ';
}
cout << t->key << endl;
printtree(t->right, h + 1);
}
}
int main()
{
}
|
3f58a92c1ebb6a0786d6bddd349763a9
|
{
"intermediate": 0.3230394124984741,
"beginner": 0.5174763798713684,
"expert": 0.15948423743247986
}
|
32,386
|
You are a Rust expert programmer. For your job interview you are asked this question: "The output of a complex algorithm produces a Vec<Vec<String>> with an estimated size of 10000 vectors. Since this process needs to be the fastest, implement the fastest and more efficient code to write all those vectors to a file.".
|
07158a39ba38ca5e30865880f3a53895
|
{
"intermediate": 0.17340117692947388,
"beginner": 0.12927384674549103,
"expert": 0.6973249912261963
}
|
32,387
|
Есть недописанная программа реализации АВЛ-дерева. Допиши её, при следующих условиях: при удалении элемента программа заменяет его на максимальный слева; обход справа налево; добавить в дерево элементы 9, 1, 2, 2, 0, 9, 0, 3, 0, 4 и вывести его. Вот программа: #include<iostream>
using namespace std;
struct Node
{
int key, count;
Node left, right;
int balance;
};
void search(int x, Node p, short h)
{
Node p1, * p2;
if (p == NULL) // слова нет в дереве; включить его
{
p = new Node;
p->key = x;
p->balance = 0;
p->left = NULL;
p->right = NULL;
}
else if (x < p->key)
{
search(x, p->left, h);
if (h != 0) // выросла левая ветвь
{
switch (p->balance)
{
case 1:
p->balance = 0;
h = 0;
break;
case 0:
p->balance = -1;
break;
case -1: // балансировка
p1 = p->left;
if (p1->balance == -1) // L-поворот
{
p->left = p->right;
p1->right = p;
p->balance = 0;
p = p1;
}
else // LR-поворот
{
p2 = p1->right;
p1->right = p2->left;
p2->left = p1;
p->left = p2->right;
p2->right = p;
if (p2->balance == -1)
p->balance = 1;
else
p->balance = 0;
if (p2->balance == 1)
p1->balance = -1;
else
p1->balance = 0;
p = p2;
}
p->balance = 0;
h = 0;
break;
}
}
}
else if (x > p->key)
{
search(x, p->right, h);
if (h != 0) // выросла правая ветвь
{
switch (p->balance)
{
case -1:
p->balance = 0;
h = false;
break;
case 0:
p->balance = 1;
break;
case 1: // балансировка
p1 = p->right;
if (p1->balance == 1) //R-поворот
{
p->right = p1->left;
p1->left = p;
p->balance = 0;
p = p1;
}
else // RL-поворот
{
p2 = p1->left;
p1->left = p2->right;
p2->right = p1;
p->right = p2->left;
p2->left = p;
if (p2->balance == 1)
p->balance = -1;
else
p->balance = 0;
if (p2->balance == -1)
p1->balance = 1;
else
p1->balance = 0;
p = p2;
}
p->balance = 0;
h = 0;
break;
}
}
}
else
{
p->count += 1;
h = 0;
}
}
void balance1(Node* p, short h)
{
Node* p1, * p2;
short b1, b2;
switch (p->balance)
{
case -1:
p->balance = 0;
break;
case 0:
p->balance = 1;
h = 0;
break;
case 1: // балансировка
p1 = p->right;
b1 = p1->balance;
if (b1 >= 0) // R-поворот
{
p->right = p1->left;
p1->left = p;
if (b1 == 0)
{
p->balance = 1;
p1->balance = -1;
h = 0;
}
else
{
p->balance = 0;
p1->balance = 0;
}
p = p1;
}
else // RL-поворот
{
p2 = p1->left;
b2 = p2->balance;
p1->left = p2->right;
p2->right = p1;
p->right = p2->left;
p2->left = p;
if (b2 == 1)
p->balance = -1;
else
p->balance = 0;
if (b2 == -1)
p1->balance = 1;
else
p1->balance = 0;
p = p2;
p2->balance = 0;
}
}
}
void balance2(Node* p, short h)
{
Node* p1, * p2;
short b1, b2;
switch (p->balance)
{
case 1:
p->balance = 0;
break;
case 0:
p->balance = 0;
h = 0;
break;
case -1:
p1 = p->left;
b1 = p1->balance;
if (b1 <= 0) // L-поворот
{
p->left = p1->right;
p1->right = p;
if (b1 == 0)
{
p->balance = -1;
p1->balance = 1;
h = 0;
}
else
{
p->balance = 0;
p1->balance = 0;
}
p = p1;
}
else // LR-поворот
{
p2 = p1->right;
b2 = p2->balance;
p1->right = p2->left;
p2->left = p1;
p->left = p2->right;
p2->right = p;
if (b2 == -1)
p->balance = 1;
else
p->balance = 0;
if (b2 == 1)
p1->balance = -1;
else
p1->balance = 0;
p = p2;
p2->balance = 0;
}
}
}
void del(Node* r, Node* q, short h)
{
if (r->right != NULL)
{
del(r->right, q, h);
if (h != 0)
balance2(r, h);
}
else
{
q->key = r->key;
q->count = r->count;
r = r->left;
h = 1;
}
}
void delet(int x, Node* p, short h)
{
Node* q;
if (p == NULL)
{
cout << “Key is not in tree” << endl;
h = 0;
}
else if (x < p->key)
{
delet(x, p->left, h);
if (h != 0)
balance1(p, h);
}
else if (x > p->key)
{
delet(x, p->right, h);
if (h != 0)
balance2(p, h);
}
else
{
q = p;
if (q->right == NULL)
{
p = q->left;
h = 1;
}
else if (q->left == NULL)
{
p = q->right;
h = 1;
}
else
{
del(q->left, q, h);
if (h != 0)
balance1(p, h);
}
}
}
Node* tree(int x)
{
}
void printtree(Node* t, short h)
{
if (t != NULL)
{
printtree(t->left, h + 1);
for (int i = 1; i <= h; i++)
{
cout << ’ ';
}
cout << t->key << endl;
printtree(t->right, h + 1);
}
}
int main()
{
}
|
2e8d9db856f52863599fcc22af5b1393
|
{
"intermediate": 0.2973635792732239,
"beginner": 0.4581441879272461,
"expert": 0.24449224770069122
}
|
32,388
|
Есть недописанная программа реализации АВЛ-дерева. Допиши её, при следующих условиях: при удалении элемента программа заменяет его на максимальный слева; обход справа налево; добавить в дерево элементы 9, 1, 2, 2, 0, 9, 0, 3, 0, 4 и вывести его. Вот программа: #include
using namespace std;
struct Node
{
int key, count;
Node left, right;
int balance;
};
void search(int x, Node p, short h)
{
Node p1, * p2;
if (p == NULL) // слова нет в дереве; включить его
{
p = new Node;
p->key = x;
p->balance = 0;
p->left = NULL;
p->right = NULL;
}
else if (x < p->key)
{
search(x, p->left, h);
if (h != 0) // выросла левая ветвь
{
switch (p->balance)
{
case 1:
p->balance = 0;
h = 0;
break;
case 0:
p->balance = -1;
break;
case -1: // балансировка
p1 = p->left;
if (p1->balance == -1) // L-поворот
{
p->left = p->right;
p1->right = p;
p->balance = 0;
p = p1;
}
else // LR-поворот
{
p2 = p1->right;
p1->right = p2->left;
p2->left = p1;
p->left = p2->right;
p2->right = p;
if (p2->balance == -1)
p->balance = 1;
else
p->balance = 0;
if (p2->balance == 1)
p1->balance = -1;
else
p1->balance = 0;
p = p2;
}
p->balance = 0;
h = 0;
break;
}
}
}
else if (x > p->key)
{
search(x, p->right, h);
if (h != 0) // выросла правая ветвь
{
switch (p->balance)
{
case -1:
p->balance = 0;
h = false;
break;
case 0:
p->balance = 1;
break;
case 1: // балансировка
p1 = p->right;
if (p1->balance == 1) //R-поворот
{
p->right = p1->left;
p1->left = p;
p->balance = 0;
p = p1;
}
else // RL-поворот
{
p2 = p1->left;
p1->left = p2->right;
p2->right = p1;
p->right = p2->left;
p2->left = p;
if (p2->balance == 1)
p->balance = -1;
else
p->balance = 0;
if (p2->balance == -1)
p1->balance = 1;
else
p1->balance = 0;
p = p2;
}
p->balance = 0;
h = 0;
break;
}
}
}
else
{
p->count += 1;
h = 0;
}
}
void balance1(Node* p, short h)
{
Node* p1, * p2;
short b1, b2;
switch (p->balance)
{
case -1:
p->balance = 0;
break;
case 0:
p->balance = 1;
h = 0;
break;
case 1: // балансировка
p1 = p->right;
b1 = p1->balance;
if (b1 >= 0) // R-поворот
{
p->right = p1->left;
p1->left = p;
if (b1 == 0)
{
p->balance = 1;
p1->balance = -1;
h = 0;
}
else
{
p->balance = 0;
p1->balance = 0;
}
p = p1;
}
else // RL-поворот
{
p2 = p1->left;
b2 = p2->balance;
p1->left = p2->right;
p2->right = p1;
p->right = p2->left;
p2->left = p;
if (b2 == 1)
p->balance = -1;
else
p->balance = 0;
if (b2 == -1)
p1->balance = 1;
else
p1->balance = 0;
p = p2;
p2->balance = 0;
}
}
}
void balance2(Node* p, short h)
{
Node* p1, * p2;
short b1, b2;
switch (p->balance)
{
case 1:
p->balance = 0;
break;
case 0:
p->balance = 0;
h = 0;
break;
case -1:
p1 = p->left;
b1 = p1->balance;
if (b1 <= 0) // L-поворот
{
p->left = p1->right;
p1->right = p;
if (b1 == 0)
{
p->balance = -1;
p1->balance = 1;
h = 0;
}
else
{
p->balance = 0;
p1->balance = 0;
}
p = p1;
}
else // LR-поворот
{
p2 = p1->right;
b2 = p2->balance;
p1->right = p2->left;
p2->left = p1;
p->left = p2->right;
p2->right = p;
if (b2 == -1)
p->balance = 1;
else
p->balance = 0;
if (b2 == 1)
p1->balance = -1;
else
p1->balance = 0;
p = p2;
p2->balance = 0;
}
}
}
void del(Node* r, Node* q, short h)
{
if (r->right != NULL)
{
del(r->right, q, h);
if (h != 0)
balance2(r, h);
}
else
{
q->key = r->key;
q->count = r->count;
r = r->left;
h = 1;
}
}
void delet(int x, Node* p, short h)
{
Node* q;
if (p == NULL)
{
cout << “Key is not in tree” << endl;
h = 0;
}
else if (x < p->key)
{
delet(x, p->left, h);
if (h != 0)
balance1(p, h);
}
else if (x > p->key)
{
delet(x, p->right, h);
if (h != 0)
balance2(p, h);
}
else
{
q = p;
if (q->right == NULL)
{
p = q->left;
h = 1;
}
else if (q->left == NULL)
{
p = q->right;
h = 1;
}
else
{
del(q->left, q, h);
if (h != 0)
balance1(p, h);
}
}
}
Node* tree(int x)
{
}
void printtree(Node* t, short h)
{
if (t != NULL)
{
printtree(t->left, h + 1);
for (int i = 1; i <= h; i++)
{
cout << ’ ';
}
cout << t->key << endl;
printtree(t->right, h + 1);
}
}
int main()
{
}
|
c289485fc32a6ed8be0cf8265d8e33e8
|
{
"intermediate": 0.33691924810409546,
"beginner": 0.4169559180736542,
"expert": 0.24612486362457275
}
|
32,389
|
Write a for loop statement C program that Validate the Goldbach conjecture: Any sufficiently large even number can
be represented by the sum of two prime numbers. For example: 4 = 2 + 2, 6 = 3 + 3, 98
= 19 + 79
|
d2ae02b6d04790441e49ba1b043d0fb4
|
{
"intermediate": 0.18315604329109192,
"beginner": 0.5486418604850769,
"expert": 0.2682020962238312
}
|
32,390
|
how to website script download a generate video in steve.ai in chrome browser
|
40aaaab4ac7e29376c28f4090c4f978f
|
{
"intermediate": 0.31486746668815613,
"beginner": 0.21742109954357147,
"expert": 0.4677114486694336
}
|
32,391
|
What would be the fastest way to write a Vec<Vec<String>> to a file? You are free to use any algorithm, trick, unsafe code, parallelization, crate, etc
|
57f5728535232b1ba2f8e5b29625aec8
|
{
"intermediate": 0.5372718572616577,
"beginner": 0.1160067617893219,
"expert": 0.346721351146698
}
|
32,392
|
Write a simple for loop statement C program that Output the sum of all integers between 200 and 400, divisible by 3 and with a bit number of 7.
|
3a0b674d5d5766d1e31d6d94e71a08bf
|
{
"intermediate": 0.0976976603269577,
"beginner": 0.7813236117362976,
"expert": 0.12097872048616409
}
|
32,393
|
What would be the fastest way to write a Vec < Vec< String > > to a file? You are free to use any algorithm, trick, unsafe code, parallelization, crate, etc
|
860471923462924d23ce379193f152fd
|
{
"intermediate": 0.542588472366333,
"beginner": 0.10325823724269867,
"expert": 0.35415324568748474
}
|
32,394
|
how to find website script in chrome browser
|
23791fc5a8b43b8005eba783b95def80
|
{
"intermediate": 0.3270955979824066,
"beginner": 0.4102259576320648,
"expert": 0.2626783847808838
}
|
32,395
|
Есть недописанная программа реализации АВЛ-дерева. Допиши её, при следующих условиях: при удалении элемента программа заменяет его на максимальный слева; обход справа налево; добавить в дерево элементы 9, 1, 2, 2, 0, 9, 0, 3, 0, 4 и вывести его. Также всё комментируй. Вот программа: #include
using namespace std;
struct Node
{
int key, count;
Node left, right;
int balance;
};
void search(int x, Node p, short h)
{
Node p1, * p2;
if (p == NULL) // слова нет в дереве; включить его
{
p = new Node;
p->key = x;
p->balance = 0;
p->left = NULL;
p->right = NULL;
}
else if (x < p->key)
{
search(x, p->left, h);
if (h != 0) // выросла левая ветвь
{
switch (p->balance)
{
case 1:
p->balance = 0;
h = 0;
break;
case 0:
p->balance = -1;
break;
case -1: // балансировка
p1 = p->left;
if (p1->balance == -1) // L-поворот
{
p->left = p->right;
p1->right = p;
p->balance = 0;
p = p1;
}
else // LR-поворот
{
p2 = p1->right;
p1->right = p2->left;
p2->left = p1;
p->left = p2->right;
p2->right = p;
if (p2->balance == -1)
p->balance = 1;
else
p->balance = 0;
if (p2->balance == 1)
p1->balance = -1;
else
p1->balance = 0;
p = p2;
}
p->balance = 0;
h = 0;
break;
}
}
}
else if (x > p->key)
{
search(x, p->right, h);
if (h != 0) // выросла правая ветвь
{
switch (p->balance)
{
case -1:
p->balance = 0;
h = false;
break;
case 0:
p->balance = 1;
break;
case 1: // балансировка
p1 = p->right;
if (p1->balance == 1) //R-поворот
{
p->right = p1->left;
p1->left = p;
p->balance = 0;
p = p1;
}
else // RL-поворот
{
p2 = p1->left;
p1->left = p2->right;
p2->right = p1;
p->right = p2->left;
p2->left = p;
if (p2->balance == 1)
p->balance = -1;
else
p->balance = 0;
if (p2->balance == -1)
p1->balance = 1;
else
p1->balance = 0;
p = p2;
}
p->balance = 0;
h = 0;
break;
}
}
}
else
{
p->count += 1;
h = 0;
}
}
void balance1(Node* p, short h)
{
Node* p1, * p2;
short b1, b2;
switch (p->balance)
{
case -1:
p->balance = 0;
break;
case 0:
p->balance = 1;
h = 0;
break;
case 1: // балансировка
p1 = p->right;
b1 = p1->balance;
if (b1 >= 0) // R-поворот
{
p->right = p1->left;
p1->left = p;
if (b1 == 0)
{
p->balance = 1;
p1->balance = -1;
h = 0;
}
else
{
p->balance = 0;
p1->balance = 0;
}
p = p1;
}
else // RL-поворот
{
p2 = p1->left;
b2 = p2->balance;
p1->left = p2->right;
p2->right = p1;
p->right = p2->left;
p2->left = p;
if (b2 == 1)
p->balance = -1;
else
p->balance = 0;
if (b2 == -1)
p1->balance = 1;
else
p1->balance = 0;
p = p2;
p2->balance = 0;
}
}
}
void balance2(Node* p, short h)
{
Node* p1, * p2;
short b1, b2;
switch (p->balance)
{
case 1:
p->balance = 0;
break;
case 0:
p->balance = 0;
h = 0;
break;
case -1:
p1 = p->left;
b1 = p1->balance;
if (b1 <= 0) // L-поворот
{
p->left = p1->right;
p1->right = p;
if (b1 == 0)
{
p->balance = -1;
p1->balance = 1;
h = 0;
}
else
{
p->balance = 0;
p1->balance = 0;
}
p = p1;
}
else // LR-поворот
{
p2 = p1->right;
b2 = p2->balance;
p1->right = p2->left;
p2->left = p1;
p->left = p2->right;
p2->right = p;
if (b2 == -1)
p->balance = 1;
else
p->balance = 0;
if (b2 == 1)
p1->balance = -1;
else
p1->balance = 0;
p = p2;
p2->balance = 0;
}
}
}
void del(Node* r, Node* q, short h)
{
if (r->right != NULL)
{
del(r->right, q, h);
if (h != 0)
balance2(r, h);
}
else
{
q->key = r->key;
q->count = r->count;
r = r->left;
h = 1;
}
}
void delet(int x, Node* p, short h)
{
Node* q;
if (p == NULL)
{
cout << “Key is not in tree” << endl;
h = 0;
}
else if (x < p->key)
{
delet(x, p->left, h);
if (h != 0)
balance1(p, h);
}
else if (x > p->key)
{
delet(x, p->right, h);
if (h != 0)
balance2(p, h);
}
else
{
q = p;
if (q->right == NULL)
{
p = q->left;
h = 1;
}
else if (q->left == NULL)
{
p = q->right;
h = 1;
}
else
{
del(q->left, q, h);
if (h != 0)
balance1(p, h);
}
}
}
Node* tree(int x)
{
}
void printtree(Node* t, short h)
{
if (t != NULL)
{
printtree(t->left, h + 1);
for (int i = 1; i <= h; i++)
{
cout << ’ ';
}
cout << t->key << endl;
printtree(t->right, h + 1);
}
}
int main()
{
}
|
f83615faf52f82d52dd15825e93c76e2
|
{
"intermediate": 0.3024440407752991,
"beginner": 0.46434903144836426,
"expert": 0.23320689797401428
}
|
32,396
|
{formData.options.map((option, index) => (
<input
key={index}
name={option${index + 1}}
id={option${index + 1}}
type="text"
value={option.description}
onChange={(e) => handleOptionChange(e, index)}
required
/>
))}
estoy usando react tengo errores en esta parte name={option${index + 1}}
id={option${index + 1}}
|
3263a223c1e8564338d21b9c9d8f47d4
|
{
"intermediate": 0.3678784966468811,
"beginner": 0.29907453060150146,
"expert": 0.33304697275161743
}
|
32,397
|
{formData.options.map((option, index) => (
<input
key={index}
name={option${index + 1}}
id={option${index + 1}}
type="text"
value={option.description}
onChange={(e) => handleOptionChange(e, index)}
required
/>
))}
tengo errores con los index
|
db142cfb377e0f51c11df244eae2fc3c
|
{
"intermediate": 0.37783753871917725,
"beginner": 0.30243149399757385,
"expert": 0.3197309076786041
}
|
32,398
|
Есть недописанная программа реализации АВЛ-дерева. Допиши её, при следующих условиях: при удалении элемента программа заменяет его на максимальный слева; обход справа налево; добавить в дерево элементы 9, 1, 2, 2, 0, 9, 0, 3, 0, 4 и вывести его. Также всё комментируй. Вот программа: #include
using namespace std;
struct Node
{
int key, count;
Node left, right;
int balance;
};
void search(int x, Node p, short h)
{
Node p1, * p2;
if (p == NULL) // слова нет в дереве; включить его
{
p = new Node;
p->key = x;
p->balance = 0;
p->left = NULL;
p->right = NULL;
}
else if (x < p->key)
{
search(x, p->left, h);
if (h != 0) // выросла левая ветвь
{
switch (p->balance)
{
case 1:
p->balance = 0;
h = 0;
break;
case 0:
p->balance = -1;
break;
case -1: // балансировка
p1 = p->left;
if (p1->balance == -1) // L-поворот
{
p->left = p->right;
p1->right = p;
p->balance = 0;
p = p1;
}
else // LR-поворот
{
p2 = p1->right;
p1->right = p2->left;
p2->left = p1;
p->left = p2->right;
p2->right = p;
if (p2->balance == -1)
p->balance = 1;
else
p->balance = 0;
if (p2->balance == 1)
p1->balance = -1;
else
p1->balance = 0;
p = p2;
}
p->balance = 0;
h = 0;
break;
}
}
}
else if (x > p->key)
{
search(x, p->right, h);
if (h != 0) // выросла правая ветвь
{
switch (p->balance)
{
case -1:
p->balance = 0;
h = false;
break;
case 0:
p->balance = 1;
break;
case 1: // балансировка
p1 = p->right;
if (p1->balance == 1) //R-поворот
{
p->right = p1->left;
p1->left = p;
p->balance = 0;
p = p1;
}
else // RL-поворот
{
p2 = p1->left;
p1->left = p2->right;
p2->right = p1;
p->right = p2->left;
p2->left = p;
if (p2->balance == 1)
p->balance = -1;
else
p->balance = 0;
if (p2->balance == -1)
p1->balance = 1;
else
p1->balance = 0;
p = p2;
}
p->balance = 0;
h = 0;
break;
}
}
}
else
{
p->count += 1;
h = 0;
}
}
void balance1(Node* p, short h)
{
Node* p1, * p2;
short b1, b2;
switch (p->balance)
{
case -1:
p->balance = 0;
break;
case 0:
p->balance = 1;
h = 0;
break;
case 1: // балансировка
p1 = p->right;
b1 = p1->balance;
if (b1 >= 0) // R-поворот
{
p->right = p1->left;
p1->left = p;
if (b1 == 0)
{
p->balance = 1;
p1->balance = -1;
h = 0;
}
else
{
p->balance = 0;
p1->balance = 0;
}
p = p1;
}
else // RL-поворот
{
p2 = p1->left;
b2 = p2->balance;
p1->left = p2->right;
p2->right = p1;
p->right = p2->left;
p2->left = p;
if (b2 == 1)
p->balance = -1;
else
p->balance = 0;
if (b2 == -1)
p1->balance = 1;
else
p1->balance = 0;
p = p2;
p2->balance = 0;
}
}
}
void balance2(Node* p, short h)
{
Node* p1, * p2;
short b1, b2;
switch (p->balance)
{
case 1:
p->balance = 0;
break;
case 0:
p->balance = 0;
h = 0;
break;
case -1:
p1 = p->left;
b1 = p1->balance;
if (b1 <= 0) // L-поворот
{
p->left = p1->right;
p1->right = p;
if (b1 == 0)
{
p->balance = -1;
p1->balance = 1;
h = 0;
}
else
{
p->balance = 0;
p1->balance = 0;
}
p = p1;
}
else // LR-поворот
{
p2 = p1->right;
b2 = p2->balance;
p1->right = p2->left;
p2->left = p1;
p->left = p2->right;
p2->right = p;
if (b2 == -1)
p->balance = 1;
else
p->balance = 0;
if (b2 == 1)
p1->balance = -1;
else
p1->balance = 0;
p = p2;
p2->balance = 0;
}
}
}
void del(Node* r, Node* q, short h)
{
if (r->right != NULL)
{
del(r->right, q, h);
if (h != 0)
balance2(r, h);
}
else
{
q->key = r->key;
q->count = r->count;
r = r->left;
h = 1;
}
}
void delet(int x, Node* p, short h)
{
Node* q;
if (p == NULL)
{
cout << “Key is not in tree” << endl;
h = 0;
}
else if (x < p->key)
{
delet(x, p->left, h);
if (h != 0)
balance1(p, h);
}
else if (x > p->key)
{
delet(x, p->right, h);
if (h != 0)
balance2(p, h);
}
else
{
q = p;
if (q->right == NULL)
{
p = q->left;
h = 1;
}
else if (q->left == NULL)
{
p = q->right;
h = 1;
}
else
{
del(q->left, q, h);
if (h != 0)
balance1(p, h);
}
}
}
Node* tree(int x)
{
}
void printtree(Node* t, short h)
{
if (t != NULL)
{
printtree(t->left, h + 1);
for (int i = 1; i <= h; i++)
{
cout << ’ ';
}
cout << t->key << endl;
printtree(t->right, h + 1);
}
}
int main()
{
}
|
0b9bd1e24ef08cb9e111ceb35c50f49f
|
{
"intermediate": 0.3024440407752991,
"beginner": 0.46434903144836426,
"expert": 0.23320689797401428
}
|
32,399
|
Now we need to know how the length of rental duration of these family-friendly movies compares to the duration that all movies are rented for. Can you provide a table with the movie titles and divide them into 4 levels (first_quarter, second_quarter, third_quarter, and final_quarter) based on the quartiles (25%, 50%, 75%) of the average rental duration(in the number of days) for movies across all categories? Make sure to also indicate the category that these family-friendly movies fall into.
|
b9a0c49ca0353995838cf085838c322a
|
{
"intermediate": 0.4153730273246765,
"beginner": 0.2669971287250519,
"expert": 0.3176298439502716
}
|
32,400
|
import pandas as pd
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
iris = sns.load_dataset(‘iris’)
from sklearn.manifold import TSNE
tsne = TSNE(n_components=2)
tsne_result = tsne.fit_transform(X)
tsne_df = pd.DataFrame(data=tsne_result, columns=['t-SNE component 1', 't-SNE component 2'])
plt.scatter(tsne_df['t-SNE component 1'], tsne_df['t-SNE component 2'])
plt.xlabel('t-SNE Component 1')
plt.ylabel('t-SNE Component 2')
plt.show()
Подкарсь точки таргетом
|
1b011eab33c83069229120e063b7f907
|
{
"intermediate": 0.4121319055557251,
"beginner": 0.288113534450531,
"expert": 0.29975461959838867
}
|
32,401
|
fig, ax = plt.subplots() # You can modify the values as per your requirement
cax = ax.imshow(cmap='coolwarm', vmin=-1, vmax=1) # Only show the values outside the range
ax.set_xticks(np.arange(len(LB)))
ax.set_yticks(np.arange(2*len(mode_list)))
ax.set_xticklabels([r'$\rho_{st}$', r'$E_b$', r'$E_c$', r'$E_s$', r'$E_t$', r'$E_f$', r'$E_{lb}$',
r'$\rho_r$', r'$E_r$', r'$K_{x}$', r'$K_{y}$', r'$K_{z}$', r'$K_{\phi}$', r'$m_{bd}$'], rotation=45, fontsize=10)
ax.set_yticklabels([r'$f_1$', r'$f_2$', r'$f_3$', r'$f_4$', r'$f_5$', r'$f_6$', r'$f_7$',
r'$MAC_1$', r'$MAC_2$', r'$MAC_3$', r'$MAC_4$', r'$MAC_5$', r'$MAC_6$', r'$MAC_7$'])
ax.set_xlabel('Inputs')
ax.set_ylabel('Outputs')
ax.set_title('Spearman Correlation Coefficient matrix')
cbar = fig.colorbar(cax)
plt.tight_layout()
plt.show()
correct this code
|
4213746cca4df3962c378c31cf1d1250
|
{
"intermediate": 0.4524705410003662,
"beginner": 0.25018438696861267,
"expert": 0.29734504222869873
}
|
32,402
|
write a javascript bot for bustabit that is counting the loses under 10x and after 20 loses under 10x bet 1 bit till 10x do that 6 times after that bet 10 bits till 10x and do that 4 times
|
c678d014cbcba8b37d92aed07ffab6b2
|
{
"intermediate": 0.2832990884780884,
"beginner": 0.19098111987113953,
"expert": 0.5257197618484497
}
|
32,403
|
I have just started creating a python module to load all the images in a path, do some basic processing, load a caption file if one exists(same filename but with the extension .txt or .caption instead, for example, "image205.png" would have a caption file "image205.txt" or "image205.caption", and done in a way to allow further extension types with unique loading to be done)
For now, my skeleton code looks like this:
|
8914cea8a7f13ea7be712c9b7f223d8c
|
{
"intermediate": 0.5556792616844177,
"beginner": 0.16684867441654205,
"expert": 0.2774720788002014
}
|
32,404
|
Перепиши код
umap_result = umap.UMAP(n_components=2).fit_transform(X)
umap_df = pd.DataFrame(umap_result, columns=['UMAP component 1', 'UMAP component 2'])
plt.scatter(umap_df['UMAP component 1'], umap_df['UMAP component 2'])
plt.xlabel('UMAP Component 1')
plt.ylabel('UMAP Component 2')
plt.show()
Что-бы было два графика как тут:
tsne = TSNE(n_components=2)
tsne_result = tsne.fit_transform(X)
tsne_df = pd.DataFrame(data=tsne_result, columns=['t-SNE component 1', 't-SNE component 2'])
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.title(f't-SNE Analysis of Iris')
plt.scatter(tsne_df['t-SNE component 1'], tsne_df['t-SNE component 2'])
plt.xlabel('t-SNE Component 1')
plt.ylabel('t-SNE Component 2')
# t-SNE with target coloring
plt.subplot(1, 2, 2)
plt.title(f't-SNE Analysis of Iris with target')
plt.scatter(tsne_df['t-SNE component 1'], tsne_df['t-SNE component 2'], c=y)
plt.xlabel('t-SNE Component 1')
plt.ylabel('t-SNE Component 2')
plt.tight_layout()
plt.show()
|
01914c5de1154edc7b199cf07aadde74
|
{
"intermediate": 0.42045751214027405,
"beginner": 0.27109217643737793,
"expert": 0.30845028162002563
}
|
32,405
|
Для вот этого:
k_values = [2, 3, 4, 5 ,6]
for k in k_values:
kmeans = KMeans(n_clusters=k, n_init=10)
kmeans.fit(X)
labels = kmeans.labels_
Сделай
fig = plt.figure(figsize=(7, 8))
ax = plt.axes(projection=‘3d’)
ax.set_xlabel(‘Petal Width’)
ax.set_ylabel(‘Petal Length’)
ax.set_zlabel(‘Sepal Width’)
ax.scatter3D(
iris[‘petal_width’],
iris[‘petal_length’],
iris[‘sepal_width’],
c=y
)
plt.show()
|
02439a407870da5f553fe7abd27a9af4
|
{
"intermediate": 0.3851293921470642,
"beginner": 0.27340102195739746,
"expert": 0.34146955609321594
}
|
32,406
|
in python i have a matrix with dimension 800x14, of my parameters called LHS_matrix (14 input parameters, 800 samples) using latin hypercube sampling.
i have a matrix Y, with dimension 800x14, containing 14 outputs for each sampling therefore 800.
how to plot the spearman matrix?
|
7f5ecac27f844551f2755560e43ffa3d
|
{
"intermediate": 0.45332005620002747,
"beginner": 0.20576049387454987,
"expert": 0.34091946482658386
}
|
32,407
|
I need your help. This is my code:
fn to_gtf(
bedline: &BedRecord,
isoforms: &HashMap<String, String>,
track: &HashMap<String, String>,
) -> Vec<String> {
let mut result: Vec<String> = Vec::new();
let gene = isoforms.get(&bedline.name).unwrap();
let fcodon = first_codon(bedline).unwrap();
let lcodon = last_codon(bedline).unwrap();
let first_utr_end = bedline.cds_start;
let last_utr_start = bedline.cds_end;
let frames = bedline.get_frames();
let cds_end: u32 = if bedline.strand == "+" && codon_complete(&lcodon) {
move_pos(bedline, lcodon.end, -3)
} else {
bedline.cds_end
};
let cds_start = if bedline.strand == "-" && codon_complete(&fcodon) {
move_pos(bedline, fcodon.start, 3)
} else {
bedline.cds_start
};
if track.values().any(|v| *v == bedline.name) {
result.push(build_gene_line(gene, bedline));
};
build_gtf_line(
bedline,
gene,
"transcript",
bedline.tx_start,
bedline.tx_end,
3,
-1,
&mut result,
);
for i in 0..bedline.exon_count as usize {
build_gtf_line(
bedline,
gene,
"exon",
bedline.exon_start[i],
bedline.exon_end[i],
3,
i as i16,
&mut result,
);
if cds_start < cds_end {
write_features(
i,
bedline,
gene,
first_utr_end,
cds_start,
cds_end,
last_utr_start,
frames[i] as u32,
&mut result,
);
}
}
if bedline.strand != "-" {
if codon_complete(&fcodon) {
write_codon(bedline, gene, "start_codon", fcodon, &mut result);
}
if codon_complete(&lcodon) {
write_codon(bedline, gene, "stop_codon", lcodon, &mut result);
}
} else {
if codon_complete(&lcodon) {
write_codon(bedline, gene, "start_codon", lcodon, &mut result);
}
if codon_complete(&fcodon) {
write_codon(bedline, gene, "stop_codon", fcodon, &mut result);
}
}
result
}
I though I was finished but checking the output I found that the coordinates of the "gene" line are actually wrong. The "gene" line coordinates (start, end) should comprise the first start and the last end (spanning all isoforms). Using my implementation I get to only write the "gene" line once, which is correct, but I fail to assign it the correct coordinates, instead I am assigning it the coordinates of one of the isoforms only. I do not what to do right now.
|
cae6ef8bf89c9eb60dd556d5d7e9d6b3
|
{
"intermediate": 0.4811726212501526,
"beginner": 0.3629131317138672,
"expert": 0.15591424703598022
}
|
32,408
|
In an earlier session you help implementing this code:
isoforms_to_genes: &HashMap<String, String>,
transcript_coordinates: &HashMap<String, (String, u32, u32, String)>,
) -> HashMap<String, (String, u32, u32, String)> {
isoforms_to_genes
.par_iter()
.fold(
|| HashMap::new(),
|mut acc: HashMap<String, (String, u32, u32, String)>, (transcript, gene)| {
if let Some(&(ref chrom, start, end, ref strand)) =
transcript_coordinates.get(transcript)
{
let entry = acc.entry(gene.clone()).or_insert((
chrom.to_string(),
start,
end,
strand.to_string(),
));
entry.1 = entry.1.min(start); // Update min start
entry.2 = entry.2.max(end); // Update max end
}
acc
},
)
.reduce(
|| HashMap::new(),
|mut a, b| {
for (gene, (chrom, start, end, strand)) in b {
let entry = a.entry(gene).or_insert((chrom, start, end, strand));
entry.1 = entry.1.min(start); // Update min start
entry.2 = entry.2.max(end); // Update max end
}
a
},
)
}
I slightly modified it to suit better for my purposes; however, I want to ask you this: that function returns:
“ENSG00000243115.3”: (“chr9”, 12764760, 12765055, “-”), “ENS
G00000251834.1”: (“chr9”, 15431898, 15432006, “-”), “ENSG00000163904.13”: (“
chr3”, 185582495, 185633551, “+”), “ENSG00000242766.1”: (“chr2”, 90082634, 9
0083291, “+”)}
is it possible to build a "gene" line for each key taking its values? I want to end with a Vec<String> where each String is formatted like this:
chr1 bed2gtf gene 11869 14409 . + . gene_id "ENSG00000290825.1";
where bed2gtf, gene, . and . are constants
|
5503d19314d0d67af5d577001098eb8c
|
{
"intermediate": 0.3909165859222412,
"beginner": 0.3038693964481354,
"expert": 0.3052140176296234
}
|
32,409
|
Есть программа реализации АВЛ-дерева. Нужно, чтобы при удалении элемента она меняла на максимальный слева. Вот программа: #include <iostream>
using namespace std;
struct Node
{
int key, count;
Node* left; // указатель на левого потомка
Node* right; // указатель на правого потомка
int balance;
};
void search(int x, Node*& p, short h)
{
if (p == NULL) // слова нет в дереве; включить его
{
p = new Node;
p->key = x;
p->count = 1;
p->balance = 0;
p->left = NULL;
p->right = NULL;
}
else if (x < p->key)
{
search(x, p->left, h);
if (h != 0) // выросла левая ветвь
{
switch (p->balance)
{
case 1:
p->balance = 0;
h = 0;
break;
case 0:
p->balance = -1;
break;
case -1: // балансировка
Node* p1 = p->left;
if (p1->balance == -1) // L-поворот
{
p->left = p1->right;
p1->right = p;
p->balance = 0;
p = p1;
}
else // LR-поворот
{
Node* p2 = p1->right;
p1->right = p2->left;
p2->left = p1;
p->left = p2->right;
p2->right = p;
if (p2->balance == -1)
p->balance = 1;
else
p->balance = 0;
if (p2->balance == 1)
p1->balance = -1;
else
p1->balance = 0;
p = p2;
}
p->balance = 0;
h = 0;
break;
}
}
}
else if (x > p->key)
{
search(x, p->right, h);
if (h != 0) // выросла правая ветвь
{
switch (p->balance)
{
case -1:
p->balance = 0;
h = 0;
break;
case 0:
p->balance = 1;
break;
case 1: // балансировка
Node* p1 = p->right;
if (p1->balance == 1) //R-поворот
{
p->right = p1->left;
p1->left = p;
p->balance = 0;
p = p1;
}
else // RL-поворот
{
Node* p2 = p1->left;
p1->left = p2->right;
p2->right = p1;
p->right = p2->left;
p2->left = p;
if (p2->balance == 1)
p->balance = -1;
else
p->balance = 0;
if (p2->balance == -1)
p1->balance = 1;
else
p1->balance = 0;
p = p2;
}
p->balance = 0;
h = 0;
break;
}
}
}
else
{
p->count += 1;
h = 0;
}
}
void balance1(Node* p, short h)
{
Node* p1, * p2;
short b1, b2;
switch (p->balance)
{
case -1:
p->balance = 0;
break;
case 0:
p->balance = 1;
h = 0;
break;
case 1: // балансировка
p1 = p->right;
b1 = p1->balance;
if (b1 >= 0) // R-поворот
{
p->right = p1->left;
p1->left = p;
if (b1 == 0)
{
p->balance = 1;
p1->balance = -1;
h = 0;
}
else
{
p->balance = 0;
p1->balance = 0;
}
p = p1;
}
else // RL-поворот
{
p2 = p1->left;
b2 = p2->balance;
p1->left = p2->right;
p2->right = p1;
p->right = p2->left;
p2->left = p;
if (b2 == 1)
p->balance = -1;
else
p->balance = 0;
if (b2 == -1)
p1->balance = 1;
else
p1->balance = 0;
p = p2;
p2->balance = 0;
}
}
}
void balance2(Node* p, short h)
{
Node* p1, * p2;
short b1, b2;
switch (p->balance)
{
case 1:
p->balance = 0;
break;
case 0:
p->balance = 0;
h = 0;
break;
case -1:
p1 = p->left;
b1 = p1->balance;
if (b1 <= 0) // L-поворот
{
p->left = p1->right;
p1->right = p;
if (b1 == 0)
{
p->balance = -1;
p1->balance = 1;
h = 0;
}
else
{
p->balance = 0;
p1->balance = 0;
}
p = p1;
}
else // LR-поворот
{
p2 = p1->right;
b2 = p2->balance;
p1->right = p2->left;
p2->left = p1;
p->left = p2->right;
p2->right = p;
if (b2 == -1)
p->balance = 1;
else
p->balance = 0;
if (b2 == 1)
p1->balance = -1;
else
p1->balance = 0;
p = p2;
p2->balance = 0;
}
}
}
void del(Node* r, Node* q, short h)
{
if (r->right != NULL)
{
del(r->right, q, h);
if (h != 0)
balance2(r, h);
}
else
{
q->key = r->key;
q->count = r->count;
r = r->left;
h = 1;
}
}
void delet(int x, Node*& p, short h)
{
Node* q;
if (p == NULL)
{
cout << "Key is not in tree" << endl;
h = 0;
}
else if (x < p->key)
{
delet(x, p->left, h);
if (h != 0)
balance1(p, h);
}
else if (x > p->key)
{
delet(x, p->right, h);
if (h != 0)
balance2(p, h);
}
else
{
q = p;
if (q->right == NULL)
{
p = q->left;
h = 1;
}
else if (q->left == NULL)
{
p = q->right;
h = 1;
}
else
{
del(q->left, q, h);
if (h != 0)
balance1(p, h);
}
}
}
void printtree(Node* t, short h)
{
if (t != NULL)
{
printtree(t->right, h + 1);
for (int i = 1; i <= h; i++)
{
cout << ' ';
}
cout << t->key << endl;
printtree(t->left, h + 1);
}
}
int main()
{
Node* root = NULL;
return 0;
}
|
60ce564d65e390cf47370f029bebbf26
|
{
"intermediate": 0.3511388897895813,
"beginner": 0.5253139138221741,
"expert": 0.12354721128940582
}
|
32,410
|
<template>
<section class="h-100 h-custom min-h-content" >
<div class="container py-5 h-100">
<div class="row d-flex justify-content-center align-items-center h-100">
<div class="col">
<div class="card border-0">
<div class="card-body p-4">
<div class="row">
<div class="col-lg-7">
<h5 class="mb-3"><router-link :to="{name:'Home'}" class="text-body"><i
class="fas fa-long-arrow-alt-left me-2"></i>Continue shopping</router-link></h5>
<hr>
<div class="d-flex justify-content-between align-items-center mb-4">
<div>
<p class="mb-0">You have {{ $store.state.cart.length }} items in your cart</p>
</div>
</div>
<div v-for="item in $store.state.cart" class="card mb-3 shadow-sm border-0" :key="item.id">
<div class="card-body">
<div class="d-flex justify-content-between">
<div class="d-flex flex-row align-items-center">
<div>
<img
:src="item.image"
class="img-fluid rounded-3" alt="Shopping item" style="width: 65px;">
</div>
<div class="ms-3">
<p>{{ item.name }}</p>
</div>
</div>
<div class="d-flex flex-row align-items-center">
<div >
<CartAddRemove :product="item"/>
</div>
</div>
<div class="d-flex flex-row align-items-center">
<div >
<h5 class="mb-0"><i class="bi bi-currency-dollar"></i>{{ item.price*item.qty }}</h5>
<small v-if="item.hasDiscount" class="text-muted text-decoration-line-through"><i class="bi bi-currency-dollar"></i>{{ item.price}}</small>
</div>
<a role="button" @click="removeItem(item)" class="ms-4" style="color: #cecece;"><i class="bi bi-trash3 h4"></i></a>
</div>
</div>
</div>
</div>
</div>
<div class="col-lg-5">
<div class="card bg-primary text-white rounded-0 border-0">
<div class="card-body">
<div class="d-flex justify-content-between align-items-center mb-4">
<h5 class="mb-0">Cart details</h5>
<i class="bi bi-cart3 h1"></i>
</div>
<hr class="my-4">
<div class="d-flex justify-content-between">
<p class="mb-2">Subtotal</p>
<p class="mb-2"><i class="bi bi-currency-dollar"></i>{{ $store.state.cartTotal }}</p>
</div>
<div class="d-flex justify-content-between mb-4">
<p class="mb-2">Total</p>
<p class="mb-2"><i class="bi bi-currency-dollar"></i>{{ $store.state.cartTotal }}</p>
</div>
<button type="button" class="btn btn-info btn-block btn-lg">
Checkout
</button>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
</template>
<script lang="ts">
import CartAddRemove from '../components/CartAddRemove.vue';
export default{
components :{CartAddRemove},
methods:{
removeItem(item: any){
this.$store.commit('addRemoveCart',{product:item,toAdd:false})
},
},
mounted(){
}
}
</script>error: Property '$store' does not exist on type '{ $: ComponentInternalInstance; $data: {}; $props: Partial<{}> & Omit<{} & VNodeProps & AllowedComponentProps & ComponentCustomProps & Readonly<...>, never>; ... 10 more ...; $watch<T extends string | ((...args: any) => any)>(source: T, cb: T extends (...args: any) => infe
|
2d164b0606b32d74c18fbdb2e92c3223
|
{
"intermediate": 0.329758882522583,
"beginner": 0.6075674295425415,
"expert": 0.0626736730337143
}
|
32,411
|
How to get all latex symbols with python
|
c5cbefaac7f2f6348e3ecfba56292817
|
{
"intermediate": 0.33369600772857666,
"beginner": 0.2834489941596985,
"expert": 0.38285499811172485
}
|
32,412
|
how to convert Arabic alphabets to its modern standard format using python code? example showed below
Unicode Isolated Final (End) Medial (Middle) Initial (Beginning)
627 FE8D FE8E
ا ﺍ ﺎ
628 FE8F FE90 FE92 FE91
ب ﺏ ﺐ ﺒ ﺑ
062A FE95 FE96 FE98 FE97
ت ﺕ ﺖ ﺘ ﺗ
062B FE99 FE9A FE9C FE9B
ث ﺙ ﺚ ﺜ ﺛ
062C FE9D FE9E FEA0 FE9F
ج ﺝ ﺞ ﺠ ﺟ
062D FEA1 FEA2 FEA4 FEA3
ح ﺡ ﺢ ﺤ ﺣ
062E FEA5 FEA6 FEA8 FEA7
خ ﺥ ﺦ ﺨ ﺧ
062F FEA9 FEAA
د ﺩ ﺪ
630 FEAB FEAC
ذ ﺫ ﺬ
631 FEAD FEAE
ر ﺭ ﺮ
632 FEAF Feb-00
ز ﺯ ﺰ
633 1-Feb 2-Feb 4-Feb 3-Feb
س ﺱ ﺲ ﺴ ﺳ
634 5-Feb 6-Feb 8-Feb 7-Feb
ش ﺵ ﺶ ﺸ ﺷ
635 9-Feb FEBA FEBC FEBB
ص ﺹ ﺺ ﺼ ﺻ
636 FEBD FEBE FEC0 FEBF
ض ﺽ ﺾ ﻀ ﺿ
637 FEC1 FEC2 FEC4 FEC3
ط ﻁ ﻂ ﻄ ﻃ
638 FEC5 FEC6 FEC8 FEC7
ظ ﻅ ﻆ ﻈ ﻇ
639 FEC9 FECA FECC FECB
ع ﻉ ﻊ ﻌ ﻋ
063A FECD FECE FED0 FECF
غ ﻍ ﻎ ﻐ ﻏ
641 FED1 FED2 FED4 FED3
ف ﻑ ﻒ ﻔ ﻓ
642 FED5 FED6 FED8 FED7
ق ﻕ ﻖ ﻘ ﻗ
643 FED9 FEDA FEDC FEDB
ك ﻙ ﻚ ﻜ ﻛ
644 FEDD FEDE FEE0 FEDF
ل ﻝ ﻞ ﻠ ﻟ
645 FEE1 FEE2 FEE4 FEE3
م ﻡ ﻢ ﻤ ﻣ
646 FEE5 FEE6 FEE8 FEE7
ن ﻥ ﻦ ﻨ ﻧ
647 FEE9 FEEA FEEC FEEB
ه ﻩ ﻪ ﻬ ﻫ
648 FEED FEEE
و ﻭ ﻮ
064A FEF1 FEF2 FEF4 FEF3
ي ﻱ ﻲ ﻴ ﻳ
622 FE81 FE82
آ ﺁ ﺂ
629 FE93 FE94 — —
ة ﺓ ﺔ
649 FEEF FEF0 — —
ى ﻯ ﻰ
|
ef0418e9f79e572088b7c9a1aefefe75
|
{
"intermediate": 0.3282982409000397,
"beginner": 0.4458399713039398,
"expert": 0.22586177289485931
}
|
32,413
|
I have this code:
def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
final_signal = []
exit = []
sell_result = 0.0
buy_result = 0.0
active_signal = None
mark_price_data = client.ticker_price(symbol=symbol)
mp = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
mark_price_data_change = client.ticker_24hr_price_change(symbol=symbol)
mark_price_percent = float(mark_price_data_change['priceChangePercent']) if 'priceChangePercent' in mark_price_data_change else 0.0
# Retrieve depth data
depth_data = client.depth(symbol=symbol)
bids = depth_data['bids']
bid_prices = [float(bid[0]) for bid in bids]
highest_bid_price = max(bid_prices)
lowest_bid_price = min(bid_prices)
b_h = highest_bid_price
buy_lowest_price = lowest_bid_price
b_l = buy_lowest_price
asks = depth_data['asks']
ask_prices = [float(ask[0]) for ask in asks]
highest_ask_price = max(ask_prices)
lowest_ask_price = min(ask_prices)
s_h = highest_ask_price
s_l = lowest_ask_price
buy_qty = round(sum(float(bid[1]) for bid in bids), 0)
sell_qty = round(sum(float(ask[1]) for ask in asks), 0)
Return this code, and give me code which will print me in [] order prices which higher than mark_price
|
1fe452da530ea15836d325f2e546d1db
|
{
"intermediate": 0.35536226630210876,
"beginner": 0.3708707392215729,
"expert": 0.2737669348716736
}
|
32,414
|
A data analyst wants to convert their R Markdown file into another format. What are their options? Select all that apply.
HTML, PDF, and Word
Slide presentation
JPEG, PNG, and GIF
Dashboard
|
bcfed6fe4cbf29aac575fc19d946336d
|
{
"intermediate": 0.3324187099933624,
"beginner": 0.2739475965499878,
"expert": 0.3936336934566498
}
|
32,415
|
Instead change the require of index.js in C:\xampp\htdocs\web\gulpfile.js to a dynamic import() which is available in all CommonJS modules
|
d08a64098148c79f96127ddab1af6cbf
|
{
"intermediate": 0.48922640085220337,
"beginner": 0.2517930865287781,
"expert": 0.25898048281669617
}
|
32,416
|
ai distribute the following numbers for me using Random Chaos Number Generator algorithm 5 numbers 1 2 3 4 5 6 7 9 11 12 13 14 15 16 18 19 20 21 22 23 24 25 26 27 28 31 32 33 34 35 36 37 39 40 42 till all are distributed no duplicate lines
|
85e55c2db70587b46af79b9c8e895353
|
{
"intermediate": 0.2576308846473694,
"beginner": 0.1783575564622879,
"expert": 0.5640115737915039
}
|
32,417
|
I used this code: depth_data = client.depth(symbol=symbol)
bids = depth_data['bids']
bid_prices = [float(bid[0]) for bid in bids]
asks = depth_data['asks']
ask_prices = [float(ask[0]) for ask in asks]
You need to give me code which will return me highest price bid_price of all bids and most lowest ask price of all asks
|
95f556cc8c4e19c781531e1bedb00bc5
|
{
"intermediate": 0.4366626739501953,
"beginner": 0.30273765325546265,
"expert": 0.2605997323989868
}
|
32,418
|
## solver.py
import numpy as np
from corner_detector.corner_detector import CornerDetector
class Solver(object):
def __init__(self):
self.alg = CornerDetector()
def solve(self, image_gray: np.ndarray) -> np.ndarray:
result = self.alg.find_corners(image_gray)
return result
## corner_detector.corner_detector.corner_detector.py
import numpy as np
from c_settings import (
ITERATIONS_NUM,
IMAGE_THRESHOLD_MIN,
IMAGE_THRESHOLD_MAX,
NOISE_LESS,
POTENTIAL_POINTS_NUM,
THRESHOLD_DISTANCE,
THRESHOLD_K,
THRESHOLD_B, TIME_MAX
)
from services.image_service import ImageService
from services.time_service import TimeService
class CornerDetector(object):
"""Class for corner detection on image."""
def __init__(
self,
potential_points_num: int = POTENTIAL_POINTS_NUM,
iterations_num: int = ITERATIONS_NUM,
threshold_distance: int = THRESHOLD_DISTANCE,
threshold_k: float = THRESHOLD_K,
threshold_b: int = THRESHOLD_B
):
self.potential_points_num = potential_points_num
self.iterations_num = iterations_num
self.threshold_distance = threshold_distance
self.threshold_k = threshold_k
self.threshold_b = threshold_b
self.image = None
self.image_threshold = None
self.time_service = TimeService(TIME_MAX)
self.random_answer = np.array([1, 1], [2, 2], [3, 3])
def __get_coords_by_threshold(
self,
image: np.ndarray,
) -> tuple:
"""Gets coordinates with intensity more than 'image_threshold'."""
coords = np.where(image > self.image_threshold)
return coords
def __get_neigh_nums_list(
self,
coords: tuple,
kernel_size: int = 3
) -> list:
"""
Gets list with number of neighborhoods by kernel.
Returns list like [[x1, y1, N1], [x2, y2, N2]],
Where N - neighborhood's number.
"""
neigh_nums = []
offset = (kernel_size - 1) // 2
for x, y in zip(coords[0], coords[1]):
if x < 0 + offset or x > self.image.shape[0] - offset:
continue
if y < 0 + offset or y > self.image.shape[1] - offset:
continue
kernel = self.image[x-offset:x+offset+1, y-offset:y+offset+1]
step_neigh_num = np.count_nonzero(kernel) - 1
neigh_nums.append([x, y, step_neigh_num])
return neigh_nums
@staticmethod
def __sort_neigh_nums_by_N(
neigh_nums: list
) -> np.ndarray:
"""
Sort neigh_nums by N parameter.
Removes N=-1, N=0 and sorts by N desc.
"""
np_neigh_nums = np.array(neigh_nums)
np_neigh_nums = np_neigh_nums[
(np_neigh_nums[:, 2] != -1) & (np_neigh_nums[:, 2] != 0)]
np_neigh_nums = np_neigh_nums[
np.argsort(-np_neigh_nums[:, 2])]
return np_neigh_nums
def __get_potential_points_coords(
self,
sorted_niegh_nums: np.ndarray
) -> np.ndarray:
"""
Gets best candidates, potential points coords only.
Returns np.ndarray like [[x1, y1], [x2, y2]].
"""
sorted_neigh_nums = np.delete(sorted_niegh_nums, 2, axis=1)
return sorted_neigh_nums[:self.potential_points_num]
def __get_best_lines(
self,
potential_points: np.ndarray
) -> np.ndarray:
"""
Gets the best combinations of lines by all points.
Line y = kx + b.
Returns np.ndarray like [[k1, b1], [k2, b2]].
"""
remaining_points = 0
best_lines = []
num_lines = 0
while num_lines < 3:
if num_lines == 0:
remaining_points = np.copy(potential_points)
print(f"remaining points: {len(remaining_points)}")
best_inlines = []
best_line = None
if len(remaining_points) == 0:
remaining_points = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
for i in range(self.iterations_num):
sample_indices = np.random.choice(
remaining_points.shape[0], size=3, replace=False)
sample_points = remaining_points[sample_indices]
sample_x = sample_points[:, 1]
sample_y = sample_points[:, 0]
coefficients = np.polyfit(sample_x, sample_y, deg=1)
distances = np.abs(remaining_points[:, 0] - np.matmul(np.vstack((remaining_points[:, 1], np.ones_like(remaining_points[:, 1]))).T, coefficients)) / np.sqrt(coefficients[0] ** 2 + 1)
inlines = np.where(distances < self.threshold_distance)[0]
is_similar = False
for line in best_lines:
diff_k = abs(coefficients[0] - line[0])
diff_b = abs(coefficients[1] - line[1])
if diff_k <= self.threshold_k and diff_b <= self.threshold_b:
is_similar = True
break
if not is_similar and len(inlines) > len(best_inlines):
best_line = coefficients
best_inlines = inlines
if best_line is not None:
best_lines.append(best_line)
remaining_points = np.delete(remaining_points, best_inlines, axis=0)
num_lines += 1
return np.array(best_lines)
def find_corners(self, image: np.ndarray) -> np.ndarray:
"""Finding triangle corners on image process."""
self.time_service.start_time()
self.image = image
zero_percentage = ImageService.calculate_zero_pixel_percentage(image)
if not self.time_service.time_check():
return self.random_answer
if zero_percentage > NOISE_LESS:
self.image_threshold = IMAGE_THRESHOLD_MIN
else:
self.image_threshold = IMAGE_THRESHOLD_MAX
# Noise i cannot realise.
if zero_percentage < 45:
return self.random_answer
coords = self.__get_coords_by_threshold(image)
if not self.time_service.time_check():
return self.random_answer
neigh_nums = self.__get_neigh_nums_list(coords)
if not self.time_service.time_check():
return self.random_answer
sorted_neigh_nums = self.__sort_neigh_nums_by_N(neigh_nums)
if not self.time_service.time_check():
return self.random_answer
potential_points = self.__get_potential_points_coords(sorted_neigh_nums)
if not self.time_service.time_check():
return self.random_answer
best_lines = self.__get_best_lines(potential_points)
if not self.time_service.time_check():
return self.random_answer
intersection_points = np.array([
np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[1][0], -1]]), np.array([-best_lines[0][1], -best_lines[1][1]])),
np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[0][1], -best_lines[2][1]])),
np.linalg.solve(np.array([[best_lines[1][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[1][1], -best_lines[2][1]]))
], dtype=np.float32)
return intersection_points
ПОЛУЧАЮ ОШИБКУ:
solver.py import ERROR: 401 Unauthorized: unsupported operand type(s) for |: 'type' and 'type'; Traceback: File "/3divi/estimators/test_estimator.py", line 42, in estimator; module = load_module_python(_vars.entrypoint); File "/3divi/estimators/est_utils.py", line 19, in load_module_python; abort(401, str(e)); File "/usr/local/lib/python3.9/site-packages/werkzeug/exceptions.py", line 881, in abort; _aborter(status, *args, **kwargs); File "/usr/local/lib/python3.9/site-packages/werkzeug/exceptions.py", line 864, in __call__; raise self.mapping[code](*args, **kwargs)
ЧТО МОЖЕТ БЫТЬ НЕ ТАК?!
|
5b98d06a067d0d6bb4275e52099697fb
|
{
"intermediate": 0.30348730087280273,
"beginner": 0.4526437819004059,
"expert": 0.243868887424469
}
|
32,419
|
Реши задачу и напиши код на python и numpy, строящий решение методом конечных элементов. Область раздели на квадраты одинакового размера.
Задача о граничном управлении в круге с дыркой
Пусть $\vec{x}=\left(x_1, x_2\right) \in \mathbb{R}^2$, где $x_1, x_2$ - координаты точки в некоторой прямоугольной декартовой системе координат. Рассмотрим также полярную систему координат $(\phi, r)$, такую,
что $x_1=r \cos \phi, x_2=r \sin \phi$. В области $\Omega=\left{(\phi, r): R_1<r<R_2, \forall \phi \in[0,2 \pi)\right}$, $0<R_1<R_2<\infty$, рассмотрим следующую систему уравнений:
\begin{gathered} U_1 \frac{\partial \Phi}{\partial x_1}+U_2 \frac{\partial \Phi}{\partial x_2}-\mu \frac{\partial^2 \Phi}{\partial x_1^2}-\mu \frac{\partial^2 \Phi}{\partial x_2^2}=f(\vec{x}), \\ U_n^{(-)} \Phi+\mu \operatorname{grad} \Phi \cdot \vec{n}+\gamma\left(\Phi-\Phi_r\right)=u \text { на } \Gamma_{\text {in }}, \\ U_n^{(-)} \Phi+\mu \operatorname{grad} \Phi \cdot \vec{n}+\gamma\left(\Phi-\Phi_r\right)=0 \text { на } \Gamma_{\text {out }}, \end{gathered}
с дополнительным условием
\Phi=\Phi_{\text {obs }} \quad \text { на } \Gamma_{\text {out }},
где $\Gamma_{\text {in }}=\left{\left(\phi, R_1\right), \forall \phi \in[0,2 \pi)\right}, \Gamma_{\text {out }}=\left{\left(\phi, R_2\right), \forall \phi \in[0,2 \pi)\right}, \vec{U}=\left(U_1, U_2\right)^T$ - вектор скорости, причем $\frac{\partial U_1}{\partial x_1}+\frac{\partial U_2}{\partial x_2}=0, U_n^{(-)}=(|\vec{U} \cdot \vec{n}|-\vec{U} \cdot \vec{n})$ - "входящий поток", $\vec{n}-$ внешняя нормаль к $\Omega, \operatorname{grad}(\Phi)=\left(\frac{\partial \Phi}{\partial x_1}, \frac{\partial \Phi}{\partial x_2}\right)^T, \mu=\mathrm{const}>0, \gamma=$ const $>0 ; f, \Phi_{o b s}, \Phi_r$ - заданные функции.
Найти: $\Phi, u$.
|
479da6288738563602f4c09d4be12e7c
|
{
"intermediate": 0.33770087361335754,
"beginner": 0.4576137959957123,
"expert": 0.20468537509441376
}
|
32,420
|
import numpy as np
from c_settings import (
ITERATIONS_NUM,
IMAGE_THRESHOLD_MIN,
IMAGE_THRESHOLD_MAX,
NOISE_LESS,
POTENTIAL_POINTS_NUM,
THRESHOLD_DISTANCE,
THRESHOLD_K,
THRESHOLD_B, TIME_MAX
)
from services.image_service import ImageService
from services.time_service import TimeService
class CornerDetector(object):
"""Class for corner detection on image."""
def __init__(
self,
potential_points_num: int = POTENTIAL_POINTS_NUM,
iterations_num: int = ITERATIONS_NUM,
threshold_distance: int = THRESHOLD_DISTANCE,
threshold_k: float = THRESHOLD_K,
threshold_b: int = THRESHOLD_B
):
self.potential_points_num = potential_points_num
self.iterations_num = iterations_num
self.threshold_distance = threshold_distance
self.threshold_k = threshold_k
self.threshold_b = threshold_b
self.image = None
self.image_threshold = None
self.time_service = TimeService(TIME_MAX)
self.random_answer = np.array([[1, 1], [2, 2], [3, 3]])
def __get_coords_by_threshold(
self,
image: np.ndarray,
) -> tuple:
"""Gets coordinates with intensity more than 'image_threshold'."""
coords = np.where(image > self.image_threshold)
return coords
def __get_neigh_nums_list(
self,
coords: tuple,
kernel_size: int = 3
) -> list:
"""
Gets list with number of neighborhoods by kernel.
Returns list like [[x1, y1, N1], [x2, y2, N2]],
Where N - neighborhood's number.
"""
neigh_nums = []
offset = (kernel_size - 1) // 2
for x, y in zip(coords[0], coords[1]):
if x < 0 + offset or x > self.image.shape[0] - offset:
continue
if y < 0 + offset or y > self.image.shape[1] - offset:
continue
kernel = self.image[x-offset:x+offset+1, y-offset:y+offset+1]
step_neigh_num = np.count_nonzero(kernel) - 1
neigh_nums.append([x, y, step_neigh_num])
return neigh_nums
@staticmethod
def __sort_neigh_nums_by_N(
neigh_nums: list
) -> np.ndarray:
"""
Sort neigh_nums by N parameter.
Removes N=-1, N=0 and sorts by N desc.
"""
np_neigh_nums = np.array(neigh_nums)
np_neigh_nums = np_neigh_nums[
(np_neigh_nums[:, 2] != -1) & (np_neigh_nums[:, 2] != 0)]
np_neigh_nums = np_neigh_nums[
np.argsort(-np_neigh_nums[:, 2])]
return np_neigh_nums
def __get_potential_points_coords(
self,
sorted_niegh_nums: np.ndarray
) -> np.ndarray:
"""
Gets best candidates, potential points coords only.
Returns np.ndarray like [[x1, y1], [x2, y2]].
"""
sorted_neigh_nums = np.delete(sorted_niegh_nums, 2, axis=1)
return sorted_neigh_nums[:self.potential_points_num]
def __get_best_lines(
self,
potential_points: np.ndarray
) -> np.ndarray:
"""
Gets the best combinations of lines by all points.
Line y = kx + b.
Returns np.ndarray like [[k1, b1], [k2, b2]].
"""
remaining_points = 0
best_lines = []
num_lines = 0
while num_lines < 3:
if not self.time_service.check_timer():
return self.random_answer
if num_lines == 0:
remaining_points = np.copy(potential_points)
print(f"remaining points: {len(remaining_points)}")
best_inlines = []
best_line = None
if len(remaining_points) == 0:
remaining_points = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
for i in range(self.iterations_num):
sample_indices = np.random.choice(
remaining_points.shape[0], size=3, replace=False)
sample_points = remaining_points[sample_indices]
sample_x = sample_points[:, 1]
sample_y = sample_points[:, 0]
coefficients = np.polyfit(sample_x, sample_y, deg=1)
distances = np.abs(remaining_points[:, 0] - np.matmul(np.vstack((remaining_points[:, 1], np.ones_like(remaining_points[:, 1]))).T, coefficients)) / np.sqrt(coefficients[0] ** 2 + 1)
inlines = np.where(distances < self.threshold_distance)[0]
is_similar = False
for line in best_lines:
diff_k = abs(coefficients[0] - line[0])
diff_b = abs(coefficients[1] - line[1])
if diff_k <= self.threshold_k and diff_b <= self.threshold_b:
is_similar = True
break
if not is_similar and len(inlines) > len(best_inlines):
best_line = coefficients
best_inlines = inlines
if best_line is not None:
best_lines.append(best_line)
remaining_points = np.delete(remaining_points, best_inlines, axis=0)
num_lines += 1
return np.array(best_lines)
def find_corners(self, image: np.ndarray) -> np.ndarray:
"""Finding triangle corners on image process."""
self.time_service.start_timer()
self.image = image
zero_percentage = ImageService.calculate_zero_pixel_percentage(image)
if zero_percentage > NOISE_LESS:
self.image_threshold = IMAGE_THRESHOLD_MIN
else:
self.image_threshold = IMAGE_THRESHOLD_MAX
# Noise i cannot realise.
if zero_percentage < 45:
return self.random_answer
coords = self.__get_coords_by_threshold(image)
neigh_nums = self.__get_neigh_nums_list(coords)
sorted_neigh_nums = self.__sort_neigh_nums_by_N(neigh_nums)
potential_points = self.__get_potential_points_coords(sorted_neigh_nums)
best_lines = self.__get_best_lines(potential_points)
intersection_points = np.array([
np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[1][0], -1]]), np.array([-best_lines[0][1], -best_lines[1][1]])),
np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[0][1], -best_lines[2][1]])),
np.linalg.solve(np.array([[best_lines[1][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[1][1], -best_lines[2][1]]))
], dtype=np.float32)
return intersection_points
Етсь алгоритм, который находит углы треугольника, нужно с помощью numpy ускорить его, оставив смысл алгоритма таким же
|
c63d72d146926fe0879d29ef772e73f3
|
{
"intermediate": 0.3574940860271454,
"beginner": 0.4044358730316162,
"expert": 0.2380700409412384
}
|
32,421
|
import numpy as np
from c_settings import (
ITERATIONS_NUM,
IMAGE_THRESHOLD_MIN,
IMAGE_THRESHOLD_MAX,
NOISE_LESS,
POTENTIAL_POINTS_NUM,
THRESHOLD_DISTANCE,
THRESHOLD_K,
THRESHOLD_B, TIME_MAX
)
from services.image_service import ImageService
from services.time_service import TimeService
class CornerDetector(object):
"""Class for corner detection on image."""
def __init__(
self,
potential_points_num: int = POTENTIAL_POINTS_NUM,
iterations_num: int = ITERATIONS_NUM,
threshold_distance: int = THRESHOLD_DISTANCE,
threshold_k: float = THRESHOLD_K,
threshold_b: int = THRESHOLD_B
):
self.potential_points_num = potential_points_num
self.iterations_num = iterations_num
self.threshold_distance = threshold_distance
self.threshold_k = threshold_k
self.threshold_b = threshold_b
self.image = None
self.image_threshold = None
self.time_service = TimeService(TIME_MAX)
self.random_answer = np.array([[1, 1], [2, 2], [3, 3]])
def __get_coords_by_threshold(
self,
image: np.ndarray,
) -> tuple:
"""Gets coordinates with intensity more than 'image_threshold'."""
coords = np.where(image > self.image_threshold)
return coords
def __get_neigh_nums_list(
self,
coords: tuple,
kernel_size: int = 3
) -> list:
"""
Gets list with number of neighborhoods by kernel.
Returns list like [[x1, y1, N1], [x2, y2, N2]],
Where N - neighborhood's number.
"""
offset = (kernel_size - 1) // 2
kernel = self.image[coords[0] - offset:coords[0] + offset + 1,
coords[1] - offset:coords[1] + offset + 1]
step_neigh_nums = np.count_nonzero(kernel, axis=(2, 3)) - 1
neigh_nums = np.concatenate(
(np.reshape(coords[0], (-1, 1)),
np.reshape(coords[1], (-1, 1)),
np.reshape(step_neigh_nums, (-1, 1))),
axis=1
)
return list(neigh_nums)
@staticmethod
def __sort_neigh_nums_by_N(
neigh_nums: list
) -> np.ndarray:
"""
Sort neigh_nums by N parameter.
Removes N=-1, N=0 and sorts by N desc.
"""
np_neigh_nums = np.array(neigh_nums)
np_neigh_nums = np_neigh_nums[np_neigh_nums[:, 2] != 0]
sorted_indices = np.argsort(-np_neigh_nums[:, 2])
np_neigh_nums = np_neigh_nums[sorted_indices]
return np_neigh_nums
def __get_potential_points_coords(
self,
sorted_niegh_nums: np.ndarray
) -> np.ndarray:
"""
Gets best candidates, potential points coords only.
Returns np.ndarray like [[x1, y1], [x2, y2]].
"""
sorted_neigh_nums = np.delete(sorted_niegh_nums, 2, axis=1)
return sorted_neigh_nums[:self.potential_points_num]
def __get_best_lines(
self,
potential_points: np.ndarray
) -> np.ndarray:
"""
Gets the best combinations of lines by all points.
Line y = kx + b.
Returns np.ndarray like [[k1, b1], [k2, b2]].
"""
remaining_points = 0
best_lines = []
num_lines = 0
while num_lines < 3:
if not self.time_service.check_timer():
return self.random_answer
if num_lines == 0:
remaining_points = np.copy(potential_points)
print(f"remaining points: {len(remaining_points)}")
best_inlines = []
best_line = None
if len(remaining_points) == 0:
remaining_points = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
for i in range(self.iterations_num):
sample_indices = np.random.choice(
remaining_points.shape[0],
size=(self.iterations_num, 3), replace=True)
sample_points = remaining_points[sample_indices]
sample_x = sample_points[:, 1]
sample_y = sample_points[:, 0]
coefficients = np.polyfit(sample_x, sample_y, deg=1)
distances = np.abs(remaining_points[:, 0] - np.matmul(np.vstack((remaining_points[:, 1], np.ones_like(remaining_points[:, 1]))).T, coefficients)) / np.sqrt(coefficients[0] ** 2 + 1)
inlines = np.where(distances < self.threshold_distance)[0]
is_similar = False
for line in best_lines:
diff_k = abs(coefficients[0] - line[0])
diff_b = abs(coefficients[1] - line[1])
if diff_k <= self.threshold_k and diff_b <= self.threshold_b:
is_similar = True
break
if not is_similar and len(inlines) > len(best_inlines):
best_line = coefficients
best_inlines = inlines
if best_line is not None:
best_lines.append(best_line)
remaining_points = np.delete(remaining_points, best_inlines, axis=0)
num_lines += 1
return np.array(best_lines)
def find_corners(self, image: np.ndarray) -> np.ndarray:
"""Finding triangle corners on image process."""
self.time_service.start_timer()
self.image = image
zero_percentage = ImageService.calculate_zero_pixel_percentage(image)
if zero_percentage > NOISE_LESS:
self.image_threshold = IMAGE_THRESHOLD_MIN
else:
self.image_threshold = IMAGE_THRESHOLD_MAX
# Noise i cannot realise.
if zero_percentage < 45:
return self.random_answer
coords = self.__get_coords_by_threshold(image)
if not self.time_service.check_timer():
return self.random_answer
neigh_nums = self.__get_neigh_nums_list(coords)
if not self.time_service.check_timer():
return self.random_answer
sorted_neigh_nums = self.__sort_neigh_nums_by_N(neigh_nums)
if not self.time_service.check_timer():
return self.random_answer
potential_points = self.__get_potential_points_coords(sorted_neigh_nums)
if not self.time_service.check_timer():
return self.random_answer
best_lines = self.__get_best_lines(potential_points)
if not self.time_service.check_timer():
return self.random_answer
intersection_points = np.linalg.solve(
np.array([[best_lines[0][0], -1], [best_lines[1][0], -1]]),
[-best_lines[0][1], -best_lines[1][1]])
intersection_points = np.vstack((intersection_points, np.linalg.solve(
np.array([[best_lines[0][0], -1], [best_lines[2][0], -1]]),
[-best_lines[0][1], -best_lines[2][1]])))
intersection_points = np.vstack((intersection_points, np.linalg.solve(
np.array([[best_lines[1][0], -1], [best_lines[2][0], -1]]),
[-best_lines[1][1], -best_lines[2][1]])))
return intersection_points
ПОЛУЧАЮ ОШИБКУ:
ERROR: only integer scalar arrays can be converted to a scalar index; Traceback: File "/3divi/estimators/test_estimator.py", line 72, in estimator; result = solver.solve(image); File "/tmp/tmpc9w_4bqy/solver.py", line 11, in solve; result = self.alg.find_corners(image_gray); File "/tmp/tmpc9w_4bqy/corner_detector/corner_detector.py", line 171, in find_corners; neigh_nums = self.__get_neigh_nums_list(coords); File "/tmp/tmpc9w_4bqy/corner_detector/corner_detector.py", line 58, in __get_neigh_nums_list; kernel = self.image[coords[0] - offset:coords[0] + offset + 1,
В чем дело?!!?
|
2b531aa6a6ecdbf868f5c0fa00bf857e
|
{
"intermediate": 0.25524914264678955,
"beginner": 0.4547785520553589,
"expert": 0.2899723947048187
}
|
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